From aa1d4d090301412e170dbb26e280dfc88ed362f8 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 2 Feb 2024 17:03:36 -0500 Subject: [PATCH 01/17] add dummy for overrides --- overrides/home.html | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 overrides/home.html diff --git a/overrides/home.html b/overrides/home.html new file mode 100644 index 0000000..e69de29 From e2423b0544246b0dbce4689195a5e07e5dd96c47 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 2 Feb 2024 17:04:57 -0500 Subject: [PATCH 02/17] fix cname --- CNAME => docs/CNAME | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename CNAME => docs/CNAME (100%) diff --git a/CNAME b/docs/CNAME similarity index 100% rename from CNAME rename to docs/CNAME From e09be645c259c355d12a62ba972cd502277bf098 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 2 Feb 2024 17:57:28 -0500 Subject: [PATCH 03/17] cleanup --- README.md | 79 ++-- docs/README.md | 5 + docs/bedbase/README.md | 27 +- docs/bedbase/bedbase-user-guide.md | 16 + docs/bedboss/README.md | 19 +- .../bedbase_configuration.yaml | 0 .../bedboss-insert.md} | 0 docs/bedboss/how-to-configure.md | 53 ++- docs/bedboss/how-to-create-database.md | 11 +- docs/bedboss_usage.md | 301 ---------------- docs/bedhost/README.md | 41 ++- docs/bedhost/guide.md | 35 -- docs/developers/bedboss.md | 82 ----- docs/developers/how_to_bedbase_config.md | 60 ---- docs/developers/how_to_create_database.md | 22 -- docs/developers/how_to_install_r_dep.md | 3 - docs/img/bedbase_logo.svg | 339 +++++++++--------- docs/users/api.md | 4 - mkdocs.yml | 56 ++- overrides/{home.html => dummy.html} | 0 20 files changed, 385 insertions(+), 768 deletions(-) create mode 100644 docs/README.md create mode 100644 docs/bedbase/bedbase-user-guide.md rename docs/{developers => bedboss}/bedbase_configuration.yaml (100%) rename docs/{developers/tutorial_insert.md => bedboss/bedboss-insert.md} (100%) delete mode 100644 docs/bedboss_usage.md delete mode 100644 docs/bedhost/guide.md delete mode 100644 docs/developers/bedboss.md delete mode 100644 docs/developers/how_to_bedbase_config.md delete mode 100644 docs/developers/how_to_create_database.md delete mode 100644 docs/developers/how_to_install_r_dep.md delete mode 100644 docs/users/api.md rename overrides/{home.html => dummy.html} (100%) diff --git a/README.md b/README.md index 8f36b36..91d4fae 100644 --- a/README.md +++ b/README.md @@ -1,29 +1,68 @@ -# Bedbase +# BEDbase documentation -## Introduction +This repository hosts the documentation for BEDbase and related tools. Please visit to view this rendered documentation. -![Test BEDBASE stack](https://github.com/databio/bedbase/workflows/Test%20BEDBASE%20stack/badge.svg) +Documentation is written using [mkdocs](https://www.mkdocs.org/) and themed with [material for mkdocs](https://squidfunk.github.io/mkdocs-material/). The repository has: -A project to aggregate, analyze, and serve genomic regions better (*aka* BED files). +- `mkdocs.yml`: the primary configuration, as well as the structure of the documentation. +- `/docs`: the markdown files. Each tool gets its own folder. -## Services +## How to write/update documentation -- API, production: https://api.bedbase.org/ -- API, dev: https://api-dev.bedbase.org/ -- Front-end, production: https://bedbase.org (alias https://bedhost-ui.pages.dev/) -- Front-end, dev: https://dev.bedbase.org -- Object store, production: https://data2.bedbase.org/ - base URL for cloudflare/backblaze +Each tool gets a `nav` section in `mkdocs.yml`, which maps to its own section/tab in the rendered documentation. So to add a new page, change titles, or change structure, edit `mkdocs.yml`. To edit the documentation itself, edit the `.md` documentation files in the subfolders under `/docs`. -## Tutorial +### Prereqs -There's a tutorial for bedbase in the [docs_jupyter](/docs_jupyter) folder (probably outdated). +``` +pip install mkdocs-material +``` -## Components -- [bedboss](https://github.com/databio/bedboss): Main BEDbase processing pipeline, combining bedqc, bedmaker, bedstat, and bedbuncher -- [bbconf](http://github.com/databio/bbconf): BEDbase configuration -- [bedhost](http://github.com/databio/bedhost): FastAPI application with API for accessing data -- [bedhost-ui](http://github.com/databio/bedhost-ui): Front-end user interface built with React -- [bedbase.org repository](https://github.com/databio/bedbase.org): Repository for deploying the bedhost container to AWS. -- [all_geo_beds](all_geo_beds): A subfolder, is the scripts to download all bed files on GEO using geofetch and build a backend to host the metadata using bedstat -- [geniml](https://github.com/databio/geniml): Machine learning for genomic intervals +### Building locally for development + +I recommend previewing your changes locally before deploying. You can get a hot-reload server going by cloning this repository, and then just running: + +``` +mkdocs serve +``` + +You can also use `mkdocs build` to build a portable local version of the docs. + + +### Publishing updates + +The documentation is published automatically upon commits to `master` using a GitHub Action, which runs `mkdocs gh-deploy`. This builds the docs, and pushes them to the `gh-pages` branch. This branch is then published with GitHub Pages. There's no need to do this locally, just let the action deploy the updates for you automatically. + +## FAQ + + +### Updating automatic documentation + +In the past, I had a plugin that would auto-document 2 things: 1. Python docs using lucidoc, and 2. Jupyter notebooks. This plugin was neat, but it caused me a lot of maintenance issues as well. So now, I've made it much simpler; now it's no longer a plugin, just a simple Python script. Update all the auto-generated docs (stored in `docs/autodoc_build`) by running the update script manually: + +```console +python update_python_autodocs.py +``` + +#### Configuring lucidoc rendering + +Auto-generated Python documentation with `lucidoc` rendering is configured in the `lucidoc` sections of `mkdocs.yml`. + +```yaml +lucidoc: + peppy: path/to/output.md +``` + +#### Configuring jupyter rendering + +Configure jupyter notebeeoks in the `jupyter` section, where you specify a list of `in` (for `.ipynb` files) and `out` (for `.md` files) locations. + +```yaml +jupyter: + - in: path/to/notebook_folder1 + out: path/to/rendered_folder1 + - in: path/to/notebook_folder2 + out: path/to/rendered_folder2 +``` + +There, you can specify which folders contain notebooks, and to where they should be rendered as markdown. diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..842ba3f --- /dev/null +++ b/docs/README.md @@ -0,0 +1,5 @@ +# Welcome to + +This site hosts developer and user documentation for components of BEDbase and related tools. Use the tab navigation above to find the project of interest. + +You can access the main BEDbase interface at . \ No newline at end of file diff --git a/docs/bedbase/README.md b/docs/bedbase/README.md index 54d35b7..2b0872b 100644 --- a/docs/bedbase/README.md +++ b/docs/bedbase/README.md @@ -1,13 +1,28 @@ +![Test BEDBASE stack](https://github.com/databio/bedbase/workflows/Test%20BEDBASE%20stack/badge.svg) + # BEDbase -BEDbase is a unifying platform for aggregating, analyzing and serving genomic region data as BED files. Input files are processed by a series of Python pipelines. The output of these pipelines is displayed through a RESTful API where users can access BED files along with useful statistics and plots. -BEDbase can be accessed in two ways: +BEDbase is a unifying platform for aggregating, analyzing and serving genomic region data as BED files. Input files are processed by a series of Python pipelines. The output of these pipelines is displayed through a RESTful API where users can access BED files along with useful statistics and plots. A project to aggregate, analyze, and serve genomic regions better (*aka* BED files). + +## Services + +- API, production: +- API, dev: +- Front-end, production: (alias ) +- Front-end, dev: +- Object store, production: - base URL for cloudflare/backblaze -## 1. The web interface +## Tutorial -Visit at +There's a tutorial for bedbase in the [docs_jupyter](/docs_jupyter) folder (probably outdated). -## 2. The API +## Components -Visit at \ No newline at end of file +- [bedboss](https://github.com/databio/bedboss): Main BEDbase processing pipeline, combining bedqc, bedmaker, bedstat, and bedbuncher +- [bbconf](http://github.com/databio/bbconf): BEDbase configuration +- [bedhost](http://github.com/databio/bedhost): FastAPI application with API for accessing data +- [bedhost-ui](http://github.com/databio/bedhost-ui): Front-end user interface built with React +- [bedbase.org repository](https://github.com/databio/bedbase.org): Repository for deploying the bedhost container to AWS. +- [all_geo_beds](all_geo_beds): A subfolder, is the scripts to download all bed files on GEO using geofetch and build a backend to host the metadata using bedstat +- [geniml](https://github.com/databio/geniml): Machine learning for genomic intervals diff --git a/docs/bedbase/bedbase-user-guide.md b/docs/bedbase/bedbase-user-guide.md new file mode 100644 index 0000000..bf43977 --- /dev/null +++ b/docs/bedbase/bedbase-user-guide.md @@ -0,0 +1,16 @@ +# BEDbase public instance + +Do we really need documentation to document the UI? + +## Finding relevant BED files + +The best way to locate data is to use the search interface on the bedbase.org home page. This search interface is smart. It relies on our [Text2BED](/geniml/tutorials/text2bednn-search-interface) models, which allow you to search the genome using natural language. We have previously computed embeddings for each BED file in BEDbase, and then you can search them. + +## Downloading data + +From the search interface, you can add results to your cart, then download all the files in your cart. + +## BEDsets + +BEDsets are collections of BED files. BEDbase holds tens of thousands of files, which span reference genome assemblies. You aren't likely to want to use all the data for one project. BEDsets provide a way to group together a subset of the files in BEDbase for a particular purpose. In the future, users will be able to create their own BEDsets. + diff --git a/docs/bedboss/README.md b/docs/bedboss/README.md index ed9a7ea..9ed6fab 100644 --- a/docs/bedboss/README.md +++ b/docs/bedboss/README.md @@ -4,7 +4,8 @@ It has 3 components: 1) bedmaker (`bedboss make`);
2) bedqc (`bedboss qc`);
-3) bedstat (`bedboss stat`). +3) bedstat (`bedboss stat`);
+4) bedbuncher (`bedboss bunch`). You may run all 3 pipelines together, or separately. @@ -44,6 +45,20 @@ It produces BED file Statistics: - **5' UTR percentage**. The percentage of the regions in the BED file that are annotated as 5'-UTR. - **3' UTR percentage**. The percentage of the regions in the BED file that are annotated as 3'-UTR. +## bedbuncher + +Pipeline designed to create **bedsets** (sets of BED files) that will be retrieved from bedbase. + +Example bedsets: +- Bed files from the AML database. +- Bed files from the [Excluderanges](https://github.com/dozmorovlab/excluderanges#bedbase-data-download) database. +- Bed files from the LOLA database. + +Bedbuncher calculates statistics: +- Bedset statistics (currenty means and standard deviations). + + +---- # Additional information ## bedmaker @@ -63,3 +78,5 @@ regionstat.R script is used to calculate the bed file statistics, so the pipelin All dependencies you can find in R helper script, and use it to easily install the required packages: - Rscript scripts/installRdeps.R [How to install R dependencies](./how_to_install_r_dep.md) + + diff --git a/docs/developers/bedbase_configuration.yaml b/docs/bedboss/bedbase_configuration.yaml similarity index 100% rename from docs/developers/bedbase_configuration.yaml rename to docs/bedboss/bedbase_configuration.yaml diff --git a/docs/developers/tutorial_insert.md b/docs/bedboss/bedboss-insert.md similarity index 100% rename from docs/developers/tutorial_insert.md rename to docs/bedboss/bedboss-insert.md diff --git a/docs/bedboss/how-to-configure.md b/docs/bedboss/how-to-configure.md index 0c19ae0..d41da3a 100644 --- a/docs/bedboss/how-to-configure.md +++ b/docs/bedboss/how-to-configure.md @@ -1,45 +1,60 @@ # How to create bedbase config file (for bedstat) ### Bedbase config file is yaml file with 4 parts: -- path to output files +- path to output files - database credentials +- qdrant credentials (can be skipped for if indexing is not needed) - server information -- remote info +- remote info (can be skipped for bedboss) ### Example: ```yaml path: - pipeline_output_path: $BEDBOSS_OUTPUT_PATH # do not change it - bedstat_dir: bedstat_output - remote_url_base: null - bedbuncher_dir: bedbucher_output - # region2vec: "add/path/here" - # vec2vec: "add/path/here" + remote_url_base: http://data.bedbase.org/ + pipeline_output_path: /data/outputs + bedstat_dir: outputs/bedstat_output + bedbuncher_dir: outputs/bedbuncher_output + region2vec: databio/r2v-ChIP-atlas-hg38 + vec2vec: databio/v2v-MiniLM-v2-ATAC-hg38 + text2vec: sentence-transformers/all-MiniLM-L6-v2 database: - host: $DB_HOST_URL - port: $POSTGRES_PORT + host: $POSTGRES_HOST + port: 5432 password: $POSTGRES_PASSWORD user: $POSTGRES_USER - name: $POSTGRES_DB + name: bedbase dialect: postgresql - driver: psycopg2 -server: - host: 0.0.0.0 - port: 8000 + driver: psycopg qdrant: - host: localhost + host: $QDRANT_HOST port: 6333 - api_key: None + api_key: $QDRANT_API_KEY collection: bedbase +server: + host: 0.0.0.0 + port: 8000 remotes: http: - prefix: https://data.bedbase.org/ + prefix: http://data.bedbase.org/ description: HTTP compatible path s3: prefix: s3://data.bedbase.org/ description: S3 compatible path +access_methods: + http: + type: "https" + description: HTTP compatible path + prefix: https://data2.bedbase.org/ + s3: + type: "s3" + description: S3 compatible path + prefix: s3://data2.bedbase.org/ + local: + type: "https" + description: How to serve local files. + prefix: /static/ ``` -### Download example bedbase configuration file here: Example bedbase configuration file +Download example bedbase configuration file here: Example bedbase configuration file . \ No newline at end of file diff --git a/docs/bedboss/how-to-create-database.md b/docs/bedboss/how-to-create-database.md index 4232000..08ee2f2 100644 --- a/docs/bedboss/how-to-create-database.md +++ b/docs/bedboss/how-to-create-database.md @@ -1,23 +1,22 @@ -# How to create BEDbase database +# How to create bedbase database -To run bedstat, bedbuncher, and bedmbed we need to create postgres database. +To run bedstat, bedbuncher and bedmbed we need to create postgres database. We are initiating postgres db in docker. If you don't have docker installed, you can install it with `sudo apt-get update && apt-get install docker-engine -y`. Now, create a persistent volume to house PostgreSQL data: -```terminal +```bash docker volume create postgres-data ``` -```terminal +```bash docker run -d --name bedbase-postgres -p 5432:5432 \ -e POSTGRES_PASSWORD=bedbasepassword \ -e POSTGRES_USER=postgres \ -e POSTGRES_DB=postgres \ - -v postgres-data:/var/lib/postgresql/data \ - postgres:13 + -v postgres-data:/var/lib/postgresql/data postgres:13 ``` Now we have created docker and can run pipelines. diff --git a/docs/bedboss_usage.md b/docs/bedboss_usage.md deleted file mode 100644 index da1003f..0000000 --- a/docs/bedboss_usage.md +++ /dev/null @@ -1,301 +0,0 @@ -# Usage reference - -BEDboss is command-line tool-warehouse of 3 pipelines for genomic interval files - -BEDboss include: bedmaker, bedqc, bedstat. This pipelines can be run using next positional arguments: - -- `bedbase all`: Runs all pipelines one in order: bedmaker -> bedqc -> bedstat - -- `bedbase insert`: Runs all pipelines one in order by using PEP file and creates bedset: bedmaker -> bedqc -> bedstat -> bedbuncher - -- `bedbase make`: Creates Bed and BigBed files from other type of genomic interval files [bigwig|bedgraph|bed|bigbed|wig] - -- `bedbase qc`: Runs Quality control for bed file (Works only with bed files) - -- `bedbase stat`: Runs statistics for bed and bigbed files. - -- `bedbase bunch`: Creates bedset from PEP file - -- `bedbase index`: Creates bed file vectors and inserts to qdrant database - -Here you can see the command-line usage instructions for the main bedboss command and for each subcommand: - -## `bedboss --help` -```console -version: 0.1.0a5 -usage: bedboss [-h] [--version] [--silent] [--verbosity V] [--logdev] - {all,insert,make,qc,stat,bunch,index} ... - -Warehouse of pipelines for BED-like files: bedmaker, bedstat, and bedqc. - -positional arguments: - {all,insert,make,qc,stat,bunch,index} - all Run all bedboss pipelines and insert data into bedbase - insert Run all bedboss pipelines using one PEP and insert - data into bedbase - make A pipeline to convert bed, bigbed, bigwig or bedgraph - files into bed and bigbed formats - qc Run quality control on bed file (bedqc) - stat A pipeline to read a file in BED format and produce - metadata in JSON format. - bunch A pipeline to create bedsets (sets of BED files) that - will be retrieved from bedbase. - index Index not indexed bed files and add them to the qdrant - database - -options: - -h, --help show this help message and exit - --version show program's version number and exit - --silent Silence logging. Overrides verbosity. - --verbosity V Set logging level (1-5 or logging module level name) - --logdev Expand content of logging message format. -``` - -## `bedboss all --help` -```console -usage: bedboss all [-h] --outfolder OUTFOLDER -s SAMPLE_NAME -f INPUT_FILE -t - INPUT_TYPE -g GENOME [-r RFG_CONFIG] - [--chrom-sizes CHROM_SIZES] [-n] [--standard-chrom] - [--check-qc] [--open-signal-matrix OPEN_SIGNAL_MATRIX] - [--ensdb ENSDB] --bedbase-config BEDBASE_CONFIG - [--treatment TREATMENT] [--cell-type CELL_TYPE] - [--description DESCRIPTION] [--no-db-commit] - [--just-db-commit] [--skip-qdrant] [-R] [-N] [-D] [-F] [-T] - [--silent] [--verbosity V] [--logdev] - -options: - -h, --help show this help message and exit - --outfolder OUTFOLDER - Pipeline output folder [Required] - -s SAMPLE_NAME, --sample-name SAMPLE_NAME - name of the sample used to systematically build the - output name [Required] - -f INPUT_FILE, --input-file INPUT_FILE - Input file [Required] - -t INPUT_TYPE, --input-type INPUT_TYPE - Input type [Required] options: - (bigwig|bedgraph|bed|bigbed|wig) - -g GENOME, --genome GENOME - reference genome (assembly) [Required] - -r RFG_CONFIG, --rfg-config RFG_CONFIG - file path to the genome config file(refgenie) - --chrom-sizes CHROM_SIZES - a full path to the chrom.sizes required for the - bedtobigbed conversion - -n, --narrowpeak whether it's a narrowpeak file - --standard-chrom Standardize chromosome names. Default: False - --check-qc Check quality control before processing data. Default: - True - --open-signal-matrix OPEN_SIGNAL_MATRIX - a full path to the openSignalMatrix required for the - tissue specificity plots - --ensdb ENSDB A full path to the ensdb gtf file required for genomes - not in GDdata - --bedbase-config BEDBASE_CONFIG - a path to the bedbase configuration file [Required] - --treatment TREATMENT - A treatment of the bed file - --cell-type CELL_TYPE - A cell type of the bed file - --description DESCRIPTION - A description of the bed file - --no-db-commit skip the JSON commit to the database - --just-db-commit just commit the JSON to the database - --skip-qdrant whether to skip qdrant indexing - -R, --recover Overwrite locks to recover from previous failed run - -N, --new-start Overwrite all results to start a fresh run - -D, --dirty Don't auto-delete intermediate files - -F, --force-follow Always run 'follow' commands - -T, --testmode Only print commands, don't run - --silent Silence logging. Overrides verbosity. - --verbosity V Set logging level (1-5 or logging module level name) - --logdev Expand content of logging message format. -``` - -## `bedboss insert --help` -```console -usage: bedboss insert [-h] --bedbase-config BEDBASE_CONFIG --pep PEP - --output-folder OUTPUT_FOLDER [-r RFG_CONFIG] - [--check-qc] [--standard-chrom] [--create-bedset] - [--skip-qdrant] [--ensdb ENSDB] [--no-db-commit] - [--just-db-commit] [--force_overwrite] [--upload-s3] - [-R] [-N] [-D] [-F] [-T] [--silent] [--verbosity V] - [--logdev] - -options: - -h, --help show this help message and exit - --bedbase-config BEDBASE_CONFIG - a path to the bedbase configuration file [Required] - --pep PEP path to the pep file or pephub registry path - containing pep [Required] - --output-folder OUTPUT_FOLDER - Pipeline output folder [Required] - -r RFG_CONFIG, --rfg-config RFG_CONFIG - file path to the genome config file(refgenie) - --check-qc Check quality control before processing data. Default: - True - --standard-chrom Standardize chromosome names. Default: False - --create-bedset Create bedset using pep samples. Name of the bedset - will be based on pep name.Default: False - --skip-qdrant whether to skip qdrant indexing - --ensdb ENSDB A full path to the ensdb gtf file required for genomes - not in GDdata - --no-db-commit skip the JSON commit to the database - --just-db-commit just commit the JSON to the database - --force_overwrite Weather to overwrite existing records. [Default: - False] - --upload-s3 Weather to upload bed, bigbed, and statistics to s3. - Before uploading you have to set up all necessury env - vars: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and - AWS_ENDPOINT_URL. [Default: False] - -R, --recover Overwrite locks to recover from previous failed run - -N, --new-start Overwrite all results to start a fresh run - -D, --dirty Don't auto-delete intermediate files - -F, --force-follow Always run 'follow' commands - -T, --testmode Only print commands, don't run - --silent Silence logging. Overrides verbosity. - --verbosity V Set logging level (1-5 or logging module level name) - --logdev Expand content of logging message format. -``` - -## `bedboss make --help` -```console -usage: bedboss make [-h] -f INPUT_FILE --outfolder OUTFOLDER [-n] -t - INPUT_TYPE -g GENOME [-r RFG_CONFIG] -o OUTPUT_BED - --output-bigbed OUTPUT_BIGBED -s SAMPLE_NAME - [--chrom-sizes CHROM_SIZES] [--standard-chrom] [-R] [-N] - [-D] [-F] [-T] [--silent] [--verbosity V] [--logdev] - -options: - -h, --help show this help message and exit - -f INPUT_FILE, --input-file INPUT_FILE - path to the input file [Required] - --outfolder OUTFOLDER - Pipeline output folder [Required] - -n, --narrowpeak whether it's a narrowpeak file - -t INPUT_TYPE, --input-type INPUT_TYPE - input file format (supported formats: bedGraph, - bigBed, bigWig, wig) [Required] - -g GENOME, --genome GENOME - reference genome [Required] - -r RFG_CONFIG, --rfg-config RFG_CONFIG - file path to the genome config file - -o OUTPUT_BED, --output-bed OUTPUT_BED - path to the output BED files [Required] - --output-bigbed OUTPUT_BIGBED - path to the folder of output bigBed files [Required] - -s SAMPLE_NAME, --sample-name SAMPLE_NAME - name of the sample used to systematically build the - output name [Required] - --chrom-sizes CHROM_SIZES - whether standardize chromosome names. If ture, - bedmaker will remove the regions on ChrUn chromosomes, - such as chrN_random and chrUn_random. [Default: False] - --standard-chrom Standardize chromosome names. Default: False - -R, --recover Overwrite locks to recover from previous failed run - -N, --new-start Overwrite all results to start a fresh run - -D, --dirty Don't auto-delete intermediate files - -F, --force-follow Always run 'follow' commands - -T, --testmode Only print commands, don't run - --silent Silence logging. Overrides verbosity. - --verbosity V Set logging level (1-5 or logging module level name) - --logdev Expand content of logging message format. -``` - -## `bedboss qc --help` -```console -usage: bedboss qc [-h] --bedfile BEDFILE --outfolder OUTFOLDER [-R] [-N] [-D] - [-F] [-T] [--silent] [--verbosity V] [--logdev] - -options: - -h, --help show this help message and exit - --bedfile BEDFILE a full path to bed file to process [Required] - --outfolder OUTFOLDER - a full path to output log folder. [Required] - -R, --recover Overwrite locks to recover from previous failed run - -N, --new-start Overwrite all results to start a fresh run - -D, --dirty Don't auto-delete intermediate files - -F, --force-follow Always run 'follow' commands - -T, --testmode Only print commands, don't run - --silent Silence logging. Overrides verbosity. - --verbosity V Set logging level (1-5 or logging module level name) - --logdev Expand content of logging message format. -``` - -## `bedboss stat --help` -```console -usage: bedboss stat [-h] --bedfile BEDFILE --outfolder OUTFOLDER - [--open-signal-matrix OPEN_SIGNAL_MATRIX] [--ensdb ENSDB] - [--bigbed BIGBED] --bedbase-config BEDBASE_CONFIG - [-y SAMPLE_YAML] --genome GENOME [--no-db-commit] - [--just-db-commit] [-R] [-N] [-D] [-F] [-T] [--silent] - [--verbosity V] [--logdev] - -options: - -h, --help show this help message and exit - --bedfile BEDFILE a full path to bed file to process [Required] - --outfolder OUTFOLDER - Pipeline output folder [Required] - --open-signal-matrix OPEN_SIGNAL_MATRIX - a full path to the openSignalMatrix required for the - tissue specificity plots - --ensdb ENSDB a full path to the ensdb gtf file required for genomes - not in GDdata - --bigbed BIGBED a full path to the bigbed files - --bedbase-config BEDBASE_CONFIG - a path to the bedbase configuration file [Required] - -y SAMPLE_YAML, --sample-yaml SAMPLE_YAML - a yaml config file with sample attributes to pass on - more metadata into the database - --genome GENOME genome assembly of the sample [Required] - --no-db-commit whether the JSON commit to the database should be - skipped - --just-db-commit whether just to commit the JSON to the database - -R, --recover Overwrite locks to recover from previous failed run - -N, --new-start Overwrite all results to start a fresh run - -D, --dirty Don't auto-delete intermediate files - -F, --force-follow Always run 'follow' commands - -T, --testmode Only print commands, don't run - --silent Silence logging. Overrides verbosity. - --verbosity V Set logging level (1-5 or logging module level name) - --logdev Expand content of logging message format. -``` - -## `bedboss bunch --help` -```console -usage: bedboss bunch [-h] --bedbase-config BEDBASE_CONFIG --bedset-name - BEDSET_NAME --bedset-pep BEDSET_PEP - [--base-api BEDBASE_API] [--cache-path CACHE_PATH] - [--heavy] - -options: - -h, --help show this help message and exit - --bedbase-config BEDBASE_CONFIG - a path to the bedbase configuration file [Required] - --bedset-name BEDSET_NAME - a name of the bedset [Required] - --bedset-pep BEDSET_PEP - bedset pep path or pephub registry path containing - bedset pep [Required] - --base-api BEDBASE_API - Bedbase API to use. Default is https://api.bedbase.org - --cache-path CACHE_PATH - Path to the cache folder. Default is ./bedabse_cache - --heavy whether to use heavy processing (Calculate and crate - plots using R script). -``` - -## `bedboss index --help` -```console -usage: bedboss index [-h] --bedbase-config BEDBASE_CONFIG - [--bedbase-api BEDBASE_API] - -options: - -h, --help show this help message and exit - --bedbase-config BEDBASE_CONFIG - a path to the bedbase configuration file [Required] - --bedbase-api BEDBASE_API - URL of the Bedbase API [Default: - https://api.bedbase.org] -``` - diff --git a/docs/bedhost/README.md b/docs/bedhost/README.md index 6abffb6..0f24129 100644 --- a/docs/bedhost/README.md +++ b/docs/bedhost/README.md @@ -1,3 +1,40 @@ -# Docs +# BEDhost API guide + +## Introduction + +You can find the formal OpenAPI documentation and interactive interface at . This document provides more conceptual introduction and explanations to how to use the API effectively. + + +## General API organization + +### Data types + +BEDbase stores two types of data, which we call *records*. They are 1. BEDs, and 2. BEDsets. BEDsets are simply collections of BEDs. Each record in the database is either a BED or a BEDset. + +### Endpoint organization + +The endpoints are divided into 3 groups: + +1. `/bed` endpoints are used to interact with metadata for BED records. +2. `/bedset` endpoints are used to interact with metadata for BEDset records. +3. `/objects` endpoints are used to download metadata and get URLs to retrieve the underlying data itself. These endpoints implement the [GA4GH DRS standard](https://ga4gh.github.io/data-repository-service-schemas/). + +Therefore, to get information and statistics about BED or BEDset records, or what is contained in the database, look through the `/bed` and `/bedset` endpoints. But if you need to write a tool that gets the actual underlying files, then you'll need to use the `/objects` endpoints. The type of identifiers used in each case differ. + +## Record identifiers vs. object identifiers + +Each record has an identifier. For example, `eaf9ee97241f300f1c7e76e1f945141f` is a BED identifier. You can use this identifier for the metadata endpoints. To download files, you'll need something slightly different -- you need an *object identifier*. This is because each BED record includes multiple files, such as the original BED file, the BigBed file, analysis plots, and so on. To download a file, you will construct what we call the `object_id`, which identifies the specific file. + +### How to construct object identifiers + +Object IDs take the form `..`. An example of an object_id for a BED file is `bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile` + +So, you can get information about this object like this: + +`GET` [/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile](/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile) + +Or, you can get a URL to download the actual file with: + +`GET` [/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http](/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http) + -These markdown files are hosted by the service to make some simple documentation for the API. \ No newline at end of file diff --git a/docs/bedhost/guide.md b/docs/bedhost/guide.md deleted file mode 100644 index 1c8899c..0000000 --- a/docs/bedhost/guide.md +++ /dev/null @@ -1,35 +0,0 @@ -# Developer Guide - -## Introduction - -### Data types - -BEDbase stores two types of data, which we call *records*. They are 1. BEDs, and 2. BEDsets. BEDsets are simply collections of BEDs. Each record in the database is either a BED or a BEDset. - -### Endpoint organization - -The endpoints are divided into 3 groups: - -1. `/bed` endpoints are used to interact with metadata for BED records. -2. `/bedset` endpoints are used to interact with metadata for BEDset records. -3. `/objects` endpoints are used to download metadata and get URLs to retrieve the underlying data itself. These endpoints implement the [GA4GH DRS standard](https://ga4gh.github.io/data-repository-service-schemas/). - -Therefore, to get information and statistics about BED or BEDset records, or what is contained in the database, look through the `/bed` and `/bedset` endpoints. But if you need to write a tool that gets the actual underlying files, then you'll need to use the `/objects` endpoints. The type of identifiers used in each case differ. - -## Record identifiers vs. object identifiers - -Each record has an identifier. For example, `eaf9ee97241f300f1c7e76e1f945141f` is a BED identifier. You can use this identifier for the metadata endpoints. To download files, you'll need something slightly different -- you need an *object identifier*. This is because each BED record includes multiple files, such as the original BED file, the BigBed file, analysis plots, and so on. To download a file, you will construct what we call the `object_id`, which identifies the specific file. - -## How to construct object identifiers - -Object IDs take the form `..`. An example of an object_id for a BED file is `bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile` - -So, you can get information about this object like this: - -`GET` [/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile](/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile) - -Or, you can get a URL to download the actual file with: - -`GET` [/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http](/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http) - - diff --git a/docs/developers/bedboss.md b/docs/developers/bedboss.md deleted file mode 100644 index 9ed6fab..0000000 --- a/docs/developers/bedboss.md +++ /dev/null @@ -1,82 +0,0 @@ -# BEDboss -bedboss is a command-line pipeline that standardizes and calculates statistics for genomic interval data, and enters the results into a BEDbase database. -It has 3 components: - -1) bedmaker (`bedboss make`);
-2) bedqc (`bedboss qc`);
-3) bedstat (`bedboss stat`);
-4) bedbuncher (`bedboss bunch`). - -You may run all 3 pipelines together, or separately. - -Mainly pipelines are intended to be run from command line but nevertheless, -they are also available as a python function, so that user can implement them to his own code. ----- -## BEDboss consist of 3 main pipelines: - -### bedmaker -bedmaker - pipeline to convert supported file types* into BED format and bigBed format. Currently supported formats: - -- bedGraph -- bigBed -- bigWig -- wig - -### bedqc -flag bed files for further evaluation to determine whether they should be included in the downstream analysis. -Currently, it flags bed files that are larger than 2G, has over 5 milliom regions, and/or has mean region width less than 10 bp. -This threshold can be changed in bedqc function arguments. - -### bedstat - -pipeline for obtaining statistics about bed files - -It produces BED file Statistics: - -- **GC content**.The average GC content of the region set. -- **Number of regions**. The total number of regions in the BED file. -- **Median TSS distance**. The median absolute distance to the Transcription Start Sites (TSS) -- **Mean region width**. The average region width of the region set. -- **Exon percentage**. The percentage of the regions in the BED file that are annotated as exon. -- **Intron percentage**. The percentage of the regions in the BED file that are annotated as intron. -- **Promoter proc percentage**. The percentage of the regions in the BED file that are annotated as promoter-prox. -- **Intergenic percentage**. The percentage of the regions in the BED file that are annotated as intergenic. -- **Promoter core percentage**. The percentage of the regions in the BED file that are annotated as promoter-core. -- **5' UTR percentage**. The percentage of the regions in the BED file that are annotated as 5'-UTR. -- **3' UTR percentage**. The percentage of the regions in the BED file that are annotated as 3'-UTR. - -## bedbuncher - -Pipeline designed to create **bedsets** (sets of BED files) that will be retrieved from bedbase. - -Example bedsets: -- Bed files from the AML database. -- Bed files from the [Excluderanges](https://github.com/dozmorovlab/excluderanges#bedbase-data-download) database. -- Bed files from the LOLA database. - -Bedbuncher calculates statistics: -- Bedset statistics (currenty means and standard deviations). - - ----- -# Additional information - -## bedmaker - -### Additional dependencies - -- bedToBigBed: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedToBigBed -- bigBedToBed: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/bigBedToBed -- bigWigToBedGraph: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/bigWigToBedGraph -- wigToBigWig: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/wigToBigWig - -## bedstat - -### Additional dependencies -regionstat.R script is used to calculate the bed file statistics, so the pipeline also depends on several R packages: - -All dependencies you can find in R helper script, and use it to easily install the required packages: - -- Rscript scripts/installRdeps.R [How to install R dependencies](./how_to_install_r_dep.md) - - diff --git a/docs/developers/how_to_bedbase_config.md b/docs/developers/how_to_bedbase_config.md deleted file mode 100644 index d41da3a..0000000 --- a/docs/developers/how_to_bedbase_config.md +++ /dev/null @@ -1,60 +0,0 @@ -# How to create bedbase config file (for bedstat) - -### Bedbase config file is yaml file with 4 parts: -- path to output files -- database credentials -- qdrant credentials (can be skipped for if indexing is not needed) -- server information -- remote info (can be skipped for bedboss) - -### Example: -```yaml -path: - remote_url_base: http://data.bedbase.org/ - pipeline_output_path: /data/outputs - bedstat_dir: outputs/bedstat_output - bedbuncher_dir: outputs/bedbuncher_output - region2vec: databio/r2v-ChIP-atlas-hg38 - vec2vec: databio/v2v-MiniLM-v2-ATAC-hg38 - text2vec: sentence-transformers/all-MiniLM-L6-v2 -database: - host: $POSTGRES_HOST - port: 5432 - password: $POSTGRES_PASSWORD - user: $POSTGRES_USER - name: bedbase - dialect: postgresql - driver: psycopg -qdrant: - host: $QDRANT_HOST - port: 6333 - api_key: $QDRANT_API_KEY - collection: bedbase -server: - host: 0.0.0.0 - port: 8000 -remotes: - http: - prefix: http://data.bedbase.org/ - description: HTTP compatible path - s3: - prefix: s3://data.bedbase.org/ - description: S3 compatible path -access_methods: - http: - type: "https" - description: HTTP compatible path - prefix: https://data2.bedbase.org/ - s3: - type: "s3" - description: S3 compatible path - prefix: s3://data2.bedbase.org/ - local: - type: "https" - description: How to serve local files. - prefix: /static/ -``` - -Download example bedbase configuration file here: Example bedbase configuration file - -. \ No newline at end of file diff --git a/docs/developers/how_to_create_database.md b/docs/developers/how_to_create_database.md deleted file mode 100644 index 08ee2f2..0000000 --- a/docs/developers/how_to_create_database.md +++ /dev/null @@ -1,22 +0,0 @@ -# How to create bedbase database - -To run bedstat, bedbuncher and bedmbed we need to create postgres database. - -We are initiating postgres db in docker. -If you don't have docker installed, you can install it with `sudo apt-get update && apt-get install docker-engine -y`. - -Now, create a persistent volume to house PostgreSQL data: - -```bash -docker volume create postgres-data -``` - -```bash -docker run -d --name bedbase-postgres -p 5432:5432 \ - -e POSTGRES_PASSWORD=bedbasepassword \ - -e POSTGRES_USER=postgres \ - -e POSTGRES_DB=postgres \ - -v postgres-data:/var/lib/postgresql/data postgres:13 -``` - -Now we have created docker and can run pipelines. diff --git a/docs/developers/how_to_install_r_dep.md b/docs/developers/how_to_install_r_dep.md deleted file mode 100644 index aa0ede2..0000000 --- a/docs/developers/how_to_install_r_dep.md +++ /dev/null @@ -1,3 +0,0 @@ -# How to install R dependencies - -Documentation about R dependencies is located in bedboss Readme: https://github.com/databio/bedboss \ No newline at end of file diff --git a/docs/img/bedbase_logo.svg b/docs/img/bedbase_logo.svg index ef042f9..275c291 100644 --- a/docs/img/bedbase_logo.svg +++ b/docs/img/bedbase_logo.svg @@ -1,12 +1,5 @@ image/svg+xml \ No newline at end of file + d="m 47.903923,39.497965 c 0.574382,-2.43312 0.9,-5.1 0,-7.5 l 12.3,-6.7 c 1.2,1.2 2.7,2 4.6,2.2 h 0.5 c 1.7,0 3.3,-0.6 4.5,-1.7 1.4,-1.2 2.2,-2.9 2.4,-4.7 0.1,-1.8 -0.5,-3.6 -1.7,-5 -1.2,-1.4 -2.9,-2.2 -4.7,-2.4 -3.8,-0.3 -7.1,2.6 -7.4,6.4 v 0 c -0.1,0.9 0,1.7 0.3,2.5 l -12.3,6.7 c -0.6,-0.8 -1.4,-1.5 -2.3,-2.1 -1.3,-0.9 -2.8,-1.4 -4.2,-1.7 v -8.3 c 1.7,-0.4 3.2,-1.4 4.2,-2.9 1,-1.5 1.4,-3.4 1.1,-5.1999996 -0.3,-1.8 -1.4,-3.4 -2.9,-4.4 -1.5,-1 -3.4,-1.4 -5.2,-1.1 -1.8,0.3 -3.4,1.4 -4.4,2.9 -2.1,3.2 -1.3,7.4999996 1.9,9.5999996 0.7,0.5 1.5,0.8 2.3,1 v 8.3 c -2.5,0.4 -4.8,1.7 -6.5,3.8 l -12.3,-6.7 c 0.5,-1.6 0.4,-3.4 -0.4,-5 -0.8,-1.7 -2.2,-2.9 -4,-3.5 -1.8,-0.6 -3.599999,-0.5 -5.2999986,0.3 -3.4,1.7 -4.9,5.8 -3.2,9.3 1.2,2.5 3.7,3.9 6.2999986,3.9 1,0 2,-0.2 3,-0.7 0.8,-0.4 1.5,-0.9 2,-1.5 l 12.3,6.7 c -1,2.5 -0.627103,5.07993 0,7.5 0.258257,0.99665 0.861495,1.89234 1.5,2.7 0.643837,0.8144 1.4,1.5 2.3,2.1 1.3,0.9 2.722195,1.38823 4.2,1.7 1.044202,0.2203 2.165698,0.36288 3.2,0.1 2.432421,-0.61824 4.784287,-1.96827 6.5,-3.8 0.713353,-0.76159 1.160253,-1.78442 1.4,-2.8 z m 13.6,-19.1 v 0 c 0.1,-2.1 2,-3.6 4,-3.5 1,0.1 1.9,0.5 2.6,1.3 0.7,0.8 1,1.7 0.9,2.7 -0.1,1 -0.5,1.9 -1.3,2.6 -0.8,0.7 -1.7,1 -2.7,0.9 -2.1,-0.1 -3.6,-1.9 -3.5,-4 z m -48.5,3.6 c -1.9,0.9 -4.0999986,0.1 -4.9999986,-1.7 -0.9,-1.9 -0.1,-4.1 1.7,-5 0.4999996,-0.3 1.0999986,-0.4 1.5999986,-0.4 0.4,0 0.8,0.1 1.2,0.2 1,0.3 1.7,1 2.2,1.9 0.9,1.9 0.1,4.1 -1.7,5 z m 22.2,-15.5999996 c 0.6,-0.8 1.4,-1.4 2.4,-1.6 0.2,0 0.5,-0.1 0.7,-0.1 0.7,0 1.5,0.2 2.1,0.7 1.7,1.2 2.2,3.4999996 1,5.1999996 -0.6,0.8 -1.4,1.4 -2.4,1.6 -1,0.2 -2,0 -2.8,-0.6 -1.7,-1.2 -2.2,-3.5 -1,-5.1999996 z m -0.9,33.3999996 c -3.3,-2.2 -4.2,-6.7 -1.9,-10 2.2,-3.3 6.7,-4.2 10,-1.9 3.3,2.2 4.2,6.7 1.9,10 -2.3,3.2 -6.8,4.1 -10,1.9 z" + id="path6" + inkscape:connector-curvature="0" + sodipodi:nodetypes="acccsccsccccccccsssscccccccsccssccaacaaaacccssscccccscccccsccsccccccc" + style="fill:#6dbdbd;stroke:#008080;fill-opacity:1;stroke-width:1;stroke-dasharray:none" /> + diff --git a/docs/users/api.md b/docs/users/api.md deleted file mode 100644 index 130287a..0000000 --- a/docs/users/api.md +++ /dev/null @@ -1,4 +0,0 @@ -# BEDbase API - -The api documentation can be found at . - diff --git a/mkdocs.yml b/mkdocs.yml index 810a253..7b899f0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -41,25 +41,12 @@ copyright: > nav: - BEDbase: - BEDbase: bedbase/README.md - - User guide: - - The BEDbase API: users/api.md - - Developer guides: - - BEDhost: + - UI guide: + - Public BEDbase instance: bedbase/bedbase-user-guide.md + - API guides: + - BEDhost API guide: - BEDhost: bedhost/README.md - - Guide: bedhost/guide.md - Changelog: bedhost/changelog.md - - BEDboss: developers/bedboss.md - - BEDboss-all pipeline: tutorial_all.md - - BEDboss insert: developers/tutorial_insert.md - - BEDmaker tutorial: bedmaker_tutorial.md - - BEDqc tutorial: bedqc_tutorial.md - - BEDstat tutorial: bedstat_tutorial.md - - Configuring bedbase: developers/how_to_bedbase_config.md - - Create bedbase postgres database: developers/how_to_create_database.md - - How to install R dependencies: developers/how_to_install_r_dep.md - - bbconf config: bbconf_config.md - - bbconf demo: bbconf_demo.md - - BEDbase tutorial: bedbase_tutorial.md - BBConf: - BBConf: bbconf/README.md - Guide: bbconf/bbc_api.md @@ -72,33 +59,34 @@ nav: - BEDboss: - BEDBoss: bedboss/README.md - How to guides: - - Configure bedboss: bedboss/how_to_bedbase_config.md + - Configure bedboss: bedboss/how-to-configure.md - Run from Python: bedboss/how-to-run-from-python.md - Install R dependencies: bedboss/how-to-install-r-dependencies.md - Create BEDbase database: bedboss/how-to-create-database.md + - BEDboss insert: bedboss/bedboss-insert.md - Reference: - Usage: bedboss/usage.md - Geniml: - Geniml: geniml/README.md - Getting Started: - - Module overviews: geniml/Rmodules.md + - Module overviews: geniml/modules.md - How-to guides: - - Assess universe fit: tutorials/assess-universe.md - - Search intervals with BEDSpace: tutorials/bedspace.md - - Evaluate embeddings: tutorials/evaluation.md - - Train region2vec embeddings: tutorials/region2vec.md - - Train single-cell embeddings: tutorials/train-scembed-model.md - - Tokenization: tutorials/tokenization.md - - Tokenize a BED file on the command line: tutorials/cli-tokenization.md - - Create consensus peaks: tutorials/create-consensus-peaks.md - - Fine-tune embeddings: tutorials/fine-tune-region2vec-model.md - - Randomize bed files: tutorials/bedshift.md - - Create evaluation dataset with bedshift: tutorials/bedshift-evaluation-guide.md + - Assess universe fit: geniml/tutorials/assess-universe.md + - Search intervals with BEDSpace: geniml/tutorials/bedspace.md + - Evaluate embeddings: geniml/tutorials/evaluation.md + - Train region2vec embeddings: geniml/tutorials/region2vec.md + - Train single-cell embeddings: geniml/tutorials/train-scembed-model.md + - Tokenization: geniml/tutorials/tokenization.md + - Tokenize a BED file on the command line: geniml/tutorials/cli-tokenization.md + - Create consensus peaks: geniml/tutorials/create-consensus-peaks.md + - Fine-tune embeddings: geniml/tutorials/fine-tune-region2vec-model.md + - Randomize bed files: geniml/tutorials/bedshift.md + - Create evaluation dataset with bedshift: geniml/tutorials/bedshift-evaluation-guide.md - Reference: - - API: autodoc_build/geniml.md - - Support: support.md - - Contributing: contributing.md - - Changelog: changelog.md + - API: geniml/autodoc_build/geniml.md + - Support: geniml/support.md + - Contributing: geniml/contributing.md + - Changelog: geniml/changelog.md - Genimtools: - Genimtools: genimtools/README.md - How to cite: citations.md diff --git a/overrides/home.html b/overrides/dummy.html similarity index 100% rename from overrides/home.html rename to overrides/dummy.html From d0dce8b050dd269aecf63d57a37ae2f782002616 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Tue, 13 Feb 2024 18:47:36 -0500 Subject: [PATCH 04/17] copy over docs again --- docs/geniml/datasets.md | 61 + .../img/geniml_tokenization_strategy.png | Bin 0 -> 371098 bytes .../img/geniml_tokenization_strategy.svg | 2187 +++++++++++++++++ docs/geniml/manuscripts.md | 17 + .../cell-type-annotation-with-knn.md | 58 + .../tutorials/integrate-with-snapatac2.md | 93 + .../load-qdrant-with-cell-embeddings.md | 82 + docs/geniml/tutorials/pre-tokenization.md | 44 + docs/geniml/tutorials/train-region2vec.md | 26 +- docs/geniml/tutorials/train-scembed-model.md | 93 +- .../use-pretrained-region2vec-model.md | 26 +- mkdocs.yml | 2 + 12 files changed, 2609 insertions(+), 80 deletions(-) create mode 100644 docs/geniml/datasets.md create mode 100644 docs/geniml/img/geniml_tokenization_strategy.png create mode 100644 docs/geniml/img/geniml_tokenization_strategy.svg create mode 100644 docs/geniml/manuscripts.md create mode 100644 docs/geniml/tutorials/cell-type-annotation-with-knn.md create mode 100644 docs/geniml/tutorials/integrate-with-snapatac2.md create mode 100644 docs/geniml/tutorials/load-qdrant-with-cell-embeddings.md create mode 100644 docs/geniml/tutorials/pre-tokenization.md diff --git a/docs/geniml/datasets.md b/docs/geniml/datasets.md new file mode 100644 index 0000000..68fc02f --- /dev/null +++ b/docs/geniml/datasets.md @@ -0,0 +1,61 @@ +# Genomic datasets with `geniml` + +## Overview +Genomic datasets are well known for their incredible size. Therefore, using these for machine learning pipelines requires clever strategies and considerations to effectively handle the large volumes of data. This is particularly problematic when training new models. To that end, `geniml` has two separate workflows for handling genomic data: one for model training and a second for model inference (using a pre-trained model). These workflows have big differences on *when* and *where* genomic datasets are tokenized and stored. + +## Model training +Model training, especially pre-training, usually requires large datasets with billions of genomic tokens. These datasets are way too large to fit in memory and therefore must be streamed from disk during training. Because of this, the data must be tokenized "on the fly" for each epoch. This is wildly inefficient and for particularly large datasets, results in the majority of training time being dedicated to tokenization alone. Therefore, the data need be *pre-tokenized* into an intermediate form which can then be streamed in for each epoch. This removes tokenization entirely from the training procedure and therefore increase efficiency (Fig. 1A). + +## Model inference +Model inference is the process of utilizing a pre-trained model to analyze some new, unseen data. While the output of the model might vary (embeddings, label, new data), the input is always the same: genomic tokens. Except under rare circumstances, it's not typical that model inference involves large volumes of data. Therefore, pre-tokenization is *not necessary*. Because of this, data is to be tokenized **in-memory** and directly to the format required to pass through the model (Fig. 1B). + +## Tokenization forms +Given the above requirements, tokenizers need to be able to output tokenized genomic data into different forms and locations. For model training: tokenizers should take either bed files or `.h5ad` single-cell datasets and convert them into an intermediary `.gtok` file format. These `.gtok` files will be directly consumed during model training. For model inference: tokenizers should take either bed files or `.h5ad` single-cell datasets and output an in-memory representation of these tokens; typically in the form of a `torch.Tensor` or python list. The following table summarizes the format, location, and scenario in which data is tokenized: + +
+ +| | Where | What | When | +| --------------- | --------- | ------------- | ----------------- | +| Model training | On disk | `.gtok` files | Prior to training | +| Model inference | In memory | `torch.Tensors` | On the fly | + +
+ +
+Tokenization strategies for model training and inference +
+ +## Datasets in `geniml` +`geniml` uses `pytorch` + `lightning` to train models. This ecosystem encourages the use of `torch`s built-in `Dataset` class to parallelize and batch the loading of data. Because training and fine-tuning models requires pre-tokenized data (`.gtok` files), `geniml` needs datasets to handle this. It most likely will look like: +```python +from typing import List + +from torch.data.utils import IterableDataset + +class PretokenizedDataset(IterableDataset): + def __init__(self, data: Union[str, List[str]): + self.data = data + if isinstance(data, str): + self._is_folder = True + elif isinstance(data, list) and isinstance(data[0], str): + self._is_folder = False + else: + raise ValueError("`data` must be a path to a folder or a list of `.gtok` files") + + def __iter__(self, indx: int): + if self._is_folder: + for file in os.listdir(self.data): + with open(file, 'r') as f: + for line in file.readlines(): + yield line.split(",") + else: + for file in self.data: + with open(file, 'r') as f: + for line in file.readlines(): + yield line.split(",") + +``` +Here, we are no longer tokenizing each epoch, rather just streaming in data that has already been pre-tokenized. I still need to think about this in the context of fine-tuning and datasets that require targets and labels. + +## `.gtok` file format +The `.gtok` file format is a binary file where each token is stored as a 32-bit integer. This allows tokens to be stored in a very compact format with the ability to represent up to 4 billion unique tokens. 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zWWxD@-8%E5s)|Y^o>OzsgvA`LCJsTUOm=i212ov4wYem8B?<7OYClSmz(HIW*>1 zYp}WNv?tZ;khP{|*fM8nS^Td^pFt+%u4+ zgby2r_-zzTAoYoI7sq5xP0izZBoMs=9FoRITY~Q|QN+Zf@@yPEs8e(cXaKI$N8ApO4rr+XuykvfO*9pM2O4NG(-IEA4-D0O}% zEG)e5$Pqr!IA7!ZMoQb;3$NJz=~eEK1C{w(2soey-2x}stx6)r3#Jb~RLt;`eI9XI z#OS^~n{;`R4u`lJ6K+x7yPrg + + +Input dataorororABC.gtok.bed.h5ad.gtok.gtok.txt++tokenstokenslabelsUnsupervised pre-trainingSupervised fine-tuningtokenizeInput dataor.bed.h5adtokenizeDataset[42, 101, 99][42, 101, 99]verborgene SchichtAusgabeschichthidden layeroutput layerverborgene SchichtAusgabeschichthidden layeroutput layerUpload to huggingfaceembeddinglabeldata`TrainingInferenceTokenizerUniverse.gtokor[42, 101, 99]Training and fine-tuningInference diff --git a/docs/geniml/manuscripts.md b/docs/geniml/manuscripts.md new file mode 100644 index 0000000..794fc0f --- /dev/null +++ b/docs/geniml/manuscripts.md @@ -0,0 +1,17 @@ +# Published manuscripts describing geniml components + +If you find `geniml` useful for your research, please cite us! It helps us convince others that our work is useful. Here is a list of published papers that describe different modules or components in the `geniml` package. + +--- + +N. J. LeRoy et al., “Fast clustering and cell-type annotation of scATACdata with pre-trained embeddings,” bioRxiv, 2023, doi: 10.1101/2023.08.01.551452. + +J. Rymuza et al., “Methods for constructing and evaluating consensus genomic interval sets,” bioRxiv, 2023, doi: 10.1101/2023.08.03.551899. + +E. Gharavi, N. J. LeRoy, G. Zheng, A. Zhang, D. E. Brown, and N. C. Sheffield, “Joint representation learning for retrieval and annotation of genomic interval sets,” bioRxiv, 2023, doi: 10.1101/2023.08.21.554131. + +G. Zheng et al., “Methods for evaluating unsupervised vector representations of genomic regions,” bioRxiv, 2023, doi: 10.1101/2023.08.03.551899. + +B. Xue, O. Khoroshevskyi, R. A. Gomez, and N. C. Sheffield, “Opportunities and challenges in sharing and reusing genomic interval data,” Frontiers in Genetics, vol. 14, 2023-03, doi: 10.3389/fgene.2023.1155809. + +E. Gharavi et al., “Embeddings of genomic region sets capture rich biological associations in low dimensions,” Bioinformatics, 2021-03, doi: 10.1093/bioinformatics/btab439. \ No newline at end of file diff --git a/docs/geniml/tutorials/cell-type-annotation-with-knn.md b/docs/geniml/tutorials/cell-type-annotation-with-knn.md new file mode 100644 index 0000000..3d887e8 --- /dev/null +++ b/docs/geniml/tutorials/cell-type-annotation-with-knn.md @@ -0,0 +1,58 @@ +# How to annotate cell-types with KNN +In the [previous tutorial](./load-qdrant-with-cell-embeddings.md), we loaded a vector database with cell embeddings. In this tutorial, we will show how to use this vector database to query cell embeddings and annotate cells with cell-type labels using a KNN classification algorithm. + +If you have **not** completed the previous tutorial, you should ensure you have a vector database with cell embeddings. + +## What is K-nearest-neighbors (KNN) classification? +According to [IBM](https://www.ibm.com/topics/knn), K-nearest-neighbors classification is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. Point more simply: KNN is a classification algorithm that uses the distance between an **unlabeled** data point and its **labed** neighbors to classify the new data point. + +Assuming we have a vector-space of well-annotated cell embeddings, we can use KNN to classify new cell embeddings based on their proximity to the labeled cell embeddings. + +## Querying the vector database +First, we need to generate new cell embeddings for the cells we want to annotate. **Note: it is imperative that the new cell embeddings are generated using the same model as the cell embeddings in the vector database.** The previous tutorial used `databio/r2v-luecken2021-hg38-v2` to generate cell embeddings. We will use the same model to generate new cell embeddings. + +```python +import scanpy as sc + +from geniml.scembed import ScEmbed + +adata = sc.read_h5ad("path/to/adata_unlabeled.h5ad") + +model = ScEmbed("databio/r2v-luecken2021-hg38-v2") +``` + +We can get embeddings of the dataset using the pre-trained model: + +```python +embeddings = model.encode(adata) + +adata.obsm['scembed_X'] = np.array(embeddings) +``` + +Now that we have the new cell embeddings, we can query the vector database to find the K-nearest-neighbors of each cell embedding. + +```python +from collections import Counter +from qdrant_client import QdrantClient + +client = QdrantClient("localhost", port=6333) + +# Query the vector database +k = 5 # set to whatever value you want, this is a hyperparameter + +for i, embedding in enumerate(embeddings): + neighbors = client.search( + collection_name="luecken2021", + query_vector=embedding.tolist(), + limit=k, + with_payload=True + ) + cell_types = [neighbor.payload["cell_type"] for neighbor in neighbors] + + # get majority + cell_type = Counter(cell_types).most_common(1)[0][0] + adata.obs['cell_type'][i] = cell_type +``` + +And just like that, we've annotated our cells with cell-type labels using KNN classification. We can improve this methodology by first clustering the unlabeled cells and then using the cluster centroids to query the vector database. This will reduce the number of queries and improve the speed of the annotation process. Another approach would be to do a secondary consensus vote on each cluster and assign one label per cluster. + diff --git a/docs/geniml/tutorials/integrate-with-snapatac2.md b/docs/geniml/tutorials/integrate-with-snapatac2.md new file mode 100644 index 0000000..0e225b7 --- /dev/null +++ b/docs/geniml/tutorials/integrate-with-snapatac2.md @@ -0,0 +1,93 @@ +# Using our models with SnapATAC2 +## Overview + +[SnapATAC2](https://github.com/kaizhang/SnapATAC2) is a flexible, versatile, and scalable single-cell omics analysis framework. It is designed to process and analyze single-cell ATAC-seq data. SnapATAC2 is written in Rust with Python bindings. It seemlessly integrates with `scanpy` and `anndata` objects. Therefore, it is extremely easy to use `geniml` models with SnapATAC2. Here's how you can do it: + +## Install tools + +Ensure that you have `geniml` and `SnapATAC2` installed. You can install both using `pip`: +```bash +pip install geniml snapatac2 +``` + +## Download some data + +To get started, let's download some single-cell ATAC-seq data. We will use the [10x Genomics PBMC 10k dataset](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard). The dataset contains 10,000 peripheral blood mononuclear cells (PBMCs) from a healthy donor. + +You can easily grab the fragment files like so: +```bash +wget "https://cf.10xgenomics.com/samples/cell-atac/2.1.0/10k_pbmc_ATACv2_nextgem_Chromium_Controller/10k_pbmc_ATACv2_nextgem_Chromium_Controller_fragments.tsv.gz" -O pbmc_fragments.tsv.gz +``` + +## Pre-process with SnapATAC2 + +Lets start by pre-processing the data with SnapATAC2. We will closely follow the [SnapATAC2 tutorial](https://kzhang.org/SnapATAC2/tutorials/pbmc.html) to get the data into an `anndata` object. + +### Import the data + +Lets import the data into `snapatac2`: +```python +from pathlib import Path +import snapatac2 as snap + +fragment_file = Path("pbmc_fragments.tsv.gz") +data = snap.pp.import_data( + fragment_file, + chrom_sizes=snap.genome.hg38, + file="pbmc.h5ad", # Optional + sorted_by_barcode=False, +) +``` +### Run some basic quality control + +Using the `snapatac2` quality control functions, we can quickly assess the quality of the data: + +```python +snap.pl.frag_size_distr(data, interactive=False) +fig = snap.pl.frag_size_distr(data, show=False) +fig.update_yaxes(type="log") +fig.show() + +snap.metrics.tsse(data, snap.genome.hg38) +snap.pl.tsse(data, interactive=False) + +snap.pp.filter_cells(data, min_counts=5000, min_tsse=10, max_counts=100000) +``` + +Next, we can add a tile matrix to the data, select features, and run `scrublet` which is a doublet detection algorithm: +```python +snap.pp.add_tile_matrix(data) +snap.pp.select_features(data, n_features=250000) +snap.pp.scrublet(data) + +# actually filter the cells +snap.pp.filter_doublets(data) +``` + +With this, we have a clean `anndata` object that we can use with `geniml`. + +### Analyze with geniml + +We will use a Region2Vec model to cluster the cells by generating embeddings. This PBMC data comes from peripheral blood mononuclear cells (PBMCs) from a healthy donor. As such. we will use the `databio/r2v-luecken2021-hg38-v2` model to generate embeddings because it contains embeddings for the Luecken2021 dataset, a first-of-its-kind multimodal benchmark dataset of 120,000 single cells from the human bone marrow of 10 diverse donors measured with two commercially-available multi-modal technologies: nuclear GEX with joint ATAC, and cellular GEX with joint ADT profiles. + +```python +import numpy as np +import scanpy as sc +from geniml.scembed import ScEmbed + +adata = sc.read_h5ad("pbmc.h5ad") +model = ScEmbed("databio/r2v-luecken2021-hg38-v2") + +adata.obsm['scembed_X'] = np.array(model.encode(adata)) +``` + +With the embeddings, we can run a usual workflow like UMAP, clustering, and visualization: +```python +sc.pp.neighbors(adata, use_rep="scembed_X") +sc.tl.umap(adata) + +sc.tl.leiden(adata) +sc.pl.umap(adata, color="leiden") +``` + +And that's it! You've now used `geniml` with SnapATAC2. You can use the embeddings to annotate cell types, or perform other analyses. If you want to learn more about this, check out the [cell-type annotation](./cell-type-annotation-with-knn.md) tutorial. diff --git a/docs/geniml/tutorials/load-qdrant-with-cell-embeddings.md b/docs/geniml/tutorials/load-qdrant-with-cell-embeddings.md new file mode 100644 index 0000000..8188c9e --- /dev/null +++ b/docs/geniml/tutorials/load-qdrant-with-cell-embeddings.md @@ -0,0 +1,82 @@ +# How to load a vector database with cell embeddings +## Overview + +In this tutorial, we will show how to load a vector database with cell embeddings. There are many benefits to storing cell-embeddings in a vector database: +1. **Speed**: Loading a vector database is much faster than re-encoding cells. +2. **Reproducibility**: You can share your cell embeddings with others. +3. **Flexibility**: You can use the same cell embeddings for many different analyses. +4. **Interoperability**: You can use the same cell embeddings with many different tools. + +In a subsequent tutorial, we will show how to use a vector database to query cell embeddings and annotate cells with cell-type labels using a KNN classification algorithm. + +## Preqrequisites +There are two core components to this tutorial: 1) the pre-trained model, and 2) the vector database. + +**Pre-trained model:** +I will be using the `databio/luecken2021` model. It was trained on the [Luecken2021](https://openreview.net/forum?id=gN35BGa1Rt) dataset, a first-of-its-kind multimodal benchmark dataset of 120,000 single cells from the human bone marrow of 10 diverse donors measured with two commercially-available multi-modal technologies: nuclear GEX with joint ATAC, and cellular GEX with joint ADT profiles. + +**Vector database:** +Vector databases are a new and exciting technology that allow you to store and query high-dimensional vectors very quickly. This tutorial will use the `qdrant` vector database. As a lab, we really like `qdrant` because it is fast, easy to use, and has a great API. You can learn more about `qdrant` [here](https://qdrant.com/). For `qdrant` setup, please refer to the [qdrant documentation](https://qdrant.com/docs/). In the end, you should have a running `qdrant` instance at `http://localhost:6333`. + +## Data preparation + +Grab a fresh copy of the Luecken2021 data from the [geo accession](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122). We want the `multiome` data. This dataset contains the binary accessibility matrix, the peaks, and the barcodes. It also conveniently contains the cell-type labels. Pre-trained models also requires that the data be in a `scanpy.AnnData` format and the `.var` attribute contain `chr`, `start`, and `end` values. + +```python +import scanpy as sc + +adata = sc.read_h5ad("path/to/adata.h5ad") +adata = adata[:, adata.var['feature_types'] == 'ATAC'] +``` + +## Getting embeddings +We can easily get embeddings of the dataset using the pre-trained model: + +```python +import scanpy as sc + +from geniml.scembed import ScEmbed + +adata = sc.read_h5ad("path/to/adata.h5ad") + +model = ScEmbed("databio/r2v-luecken2021-hg38-v2") +embeddings = model.encode(adata) + +adata.obsm['scembed_X'] = np.array(embeddings) +``` + +## Loading the vector database +With the embeddings, we can now upsert them to `qdrant`. Ensure you have `qdrant_client` installed: + +```bash +pip install qdrant-client +``` + +```python +from qdrant_client import QdrantClient +from qdrant_client.http.models import Distance, VectorParams, PointStruct + +client = QdrantClient("localhost", port=6333) + +client.create_collection( + collection_name="luecken2021", + vectors_config=VectorParams(size=embeddings.shape[1], distance=Distance.DOT), +) + +embeddings, cell_types = adata.obsm['scembed_X'], adata.obs['cell_type'] + +points = [] +for embedding, cell_type, i in zip(embeddings, cell_types, range(len(embeddings)): + points.append( + PointStruct( + id=adata.obs.index[i], + vector=embedding.tolist(), + payload={"cell_type": cell_type} + + )) + + +client.upsert(collection_name="luecken2021", points=points, wait=True) +``` + +You should now have a vector database with cell embeddings. In the next tutorial, we will show how to use this vector database to query cell embeddings and annotate cells with cell-type labels using a KNN classification algorithm. diff --git a/docs/geniml/tutorials/pre-tokenization.md b/docs/geniml/tutorials/pre-tokenization.md new file mode 100644 index 0000000..1998b9f --- /dev/null +++ b/docs/geniml/tutorials/pre-tokenization.md @@ -0,0 +1,44 @@ +# Pre-tokening training data +## Overview + +Before we train a model, we must do what is called *pre-tokenization*. Pre-tokenziation is the process of converting raw genomic region data into lists of tokens and saved into a special file format we call `.gtok` (genomic token) files. These are binary files that contain the tokenized data in the form of integers. The `.gtok` files are used directly to train the model. There are several benefits to this, including: +1. **Speed**: The tokenization process can be slow, especially when you have a lot of data. By pre-tokenizing the data, you only have to do this once. Then, you can use the `.gtok` files to train the model as many times as you want. +2. **Memory**: The `.gtok` files are much smaller than the original data. This means that you can store more data in memory and train larger models. Moreover, this enables streaming the data from disk, which is useful when you have a lot of data. +3. **Reproducibility**: By saving the tokenized data, you can ensure that the same data is used to train the model every time. This is important for reproducibility. + +## How to pretokenize data +Pretokenizing data is easy. You can use the built-in tokenizers and utilities in `geniml` to do this. Here is an example of how to pretokenize a bed file: + +```python +from genimtools.utils import write_tokens_to_gtok +from geniml.tokenization import ITTokenizer + +# instantiate a tokenizer +tokenizer = ITTokenizer("path/to/universe.bed") + +# get tokens +tokens = tokenizer.tokenize("path/to/bedfile.bed") +write_tokens_to_gtok(tokens.ids, "path/to/bedfile.gtok") +``` + +Thats it! Now you can use the `.gtok` file to train a model. + +## How to use the `.gtok` files + +To facilitate working with `.gtok` files, we have some helper-classes that can be used to train a model directly from `.gtok` files. For example, you can use teh `Region2VecDataset` class to load the `.gtok` files and train a model. See the [training documentation](./train-region2vec.md) for more information. + +```python +from geniml.region2vec.utils import Region2VecDataset + +tokens_dir = "path/to/tokens" +dataset = Region2VecDataset(tokens_dir) + +for tokens in dataset: + # train the model + print(tokens) # [42, 101, 99, ...] +``` + +## Caveats and considerations +When pretokenizing data, you should consider the following: +1. Tokens are specific to the universe that the tokenizer was trained on. If you use a different universe, you will get different tokens. This means that you should use the same universe to pretokenize the data as you will use to train the model. +2. The `.gtok` files are binary files. This means that they are not human-readable. You should keep the original bed files as well as the `.gtok` files. This is important for reproducibility and for debugging. \ No newline at end of file diff --git a/docs/geniml/tutorials/train-region2vec.md b/docs/geniml/tutorials/train-region2vec.md index 2de4f56..5c7a47b 100644 --- a/docs/geniml/tutorials/train-region2vec.md +++ b/docs/geniml/tutorials/train-region2vec.md @@ -1,11 +1,13 @@ # How to train a new Region2Vec model Region2Vec is an unsupervised method for creating embeddings of genomic regions and genomic region sets. This tutorial discusses how to train a new model. To learn how to use a pre-trained model see the [region2vec usage documentation](./use-pretrained-region2vec-model.md). -## Training data and tokenization -Training a model requires two things: 1) a set of genomic regions and 2) a tokenizer. The tokenizer is used to convert the genomic regions into tokens. The tokens are then used to train the model. A safe choice for the tokenizer is the tiled hg38 genome. However, you can define your own tokenizer if you want to use a different genome or if you want to use a different tokenization strategy. +## Training data and universe +Training a model requires two things: 1) a set of pre-tokenized data and 2) a universe. The universe is a set of regions that the model will be trained on. The universe is used to create the tokenizer, which is used to convert the raw data into tokens. The universe should be representative of the data that you will be training the model on. For example, if you are training a model on human data, you should use a universe that contains human regions. If you dont have a universe, a safe bet is to use the 1000 tiles hg38 genome. You can download the 1000 tiles hg38 genome [here](https://big.databio.org/geniml/universes/tiles1000.hg38.bed). +The pre-tokenized data is a set of `.gtok` files. These are binary files that contain the tokenized data in the form of integers. The `.gtok` files are used directly to train the model. If you have not pre-tokenized your data, see the [pre-tokenization documentation](./pre-tokenization.md). + ## Training a model ### Instantiate a new model To begin, create a new model from `Region2VecExModel`. @@ -27,7 +29,7 @@ logging.basicConfig(level=logging.INFO) # get the paths to data universe_path = os.path.expandvars("$RESOURCES/regions/genome_tiles/tiles1000.hg38.bed") -data_path = os.path.expandvars("$DATA/ChIP-Atlas/hg38/ATAC_seq/") +data_path = os.path.expandvars("$DATA/ChIP-Atlas/hg38/ATAC_seq/tokens") model = Region2VecExModel( tokenizer=ITTokenizer(universe_path), @@ -35,24 +37,20 @@ model = Region2VecExModel( ``` ### Training data -Create a list of `RegionSet`s by supplying paths to bed files: + +The training data is a set of `.gtok` files. You can use the `Region2VecDataset` class to load the `.gtok` files and train the model. ```python -# get list of all files in the data directory -# convert to backed RegionSet objects -files = os.listdir(data_path) -data = [ - RegionSet(os.path.join(data_path, f), backed=True) - for f in track(files, total=len(files)) -] -``` +from geniml.region2vec.utils import Region2VecDataset -> Note: Setting `backed=True` means that the data will be loaded into memory lazily. This is useful when you have a lot of data and you don't want to load it all into memory at once. +dataset = Region2VecDataset(data_path) +``` ## Training + Now, simply give the model the list of `RegionSet`s and run the training: ```python # train the model -model.train(data, epochs=100) +model.train(dataset, epochs=100) ``` You can export your model using the `export` function: diff --git a/docs/geniml/tutorials/train-scembed-model.md b/docs/geniml/tutorials/train-scembed-model.md index 60537e0..a18bb80 100644 --- a/docs/geniml/tutorials/train-scembed-model.md +++ b/docs/geniml/tutorials/train-scembed-model.md @@ -6,6 +6,7 @@ For this example we are using the [10x Genomics PBMC 10k dataset](https://www.10 ## Installation + Simply install the parent package `geniml` from PyPi: ```bash @@ -15,27 +16,9 @@ pip install geniml Then import `scEmbed` from `geniml`: ```python -from geniml.scembed import SCEmbed -``` - -## Usage -`scEmbed` is simple to use. Import your `AnnData` object and pass it to `SCEmbed`. `scEmbed` will work regardless of any `var` or `obs` annotations, but it is recommended that you use `scEmbed` after you have already performed some basic filtering and quality control on your data. Further. If you'd like to maintain information about region embeddings, it is recommended that you attach `chr`, `start`, adn `end` annotations to your `AnnData` object before passing it to `scEmbed`. - -```python -import scanpy as sc -from geniml.scembed import SCEmbed - -adata = sc.read_h5ad("path/to/adata.h5ad") - -scEmbed = SCEmbed(adata) -scEmbed.train( - epochs=100, -) - -cell_embeddings = scEmbed.get_cell_embeddings() +from geniml.scembed import ScEmbed ``` - ## Data preparation `scembed` requires that the input data is in the [AnnData](https://anndata.readthedocs.io/en/latest/) format. Moreover, the `.var` attribute of this object must have `chr`, `start`, and `end` values. The reason is two fold: 1) we can track which vectors belong to which genmomic regions, and 2) region vectors are now reusable. We ned three files: 1) The `barcodes.txt` file, 2) the `peaks.bed` file, and 3) the `matrix.mtx` file. These will be used to create the `AnnData` object. To begin, download the data from the 10x Genomics website: @@ -65,57 +48,71 @@ adata.write_h5ad("pbmc.h5ad") We will use the `pbmc.h5ad` file for downstream work. -## Training the model -To train an `scembed` model, just create an instance of the `SCEmbed` model class, define your hyperparamters, and call the `train` method. For this example, we will use the default hyperparameters. The only thing we need to specify is the number of epochs to train for. We will train for 10 epochs: +## Training + +Training an `scEmbed` model requires two key steps: 1) pre-tokenizing the data, and 2) training the model. + +### Pre-tokenizing the data +To learn more about pre-tokenizing the data, see the [pre-tokenization tutorial](./pre-tokenization.md). Pre-tokenization offers many benefits, the two most important being 1) speeding up training, and 2) lower resource requirements. The pre-tokenization process is simple and can be done with a combination of `geniml` and `genimtools` utilities. Here is an example of how to pre-tokenize the 10x Genomics PBMC 10k dataset: ```python -import logging +from genimtools.utils import write_tokens_to_gtok +from geniml.tokenization import ITTokenizer -import scanpy as sc -from geniml.scembed import SCEmbed +adata = sc.read_h5ad("path/to/adata.h5ad") +tokenizer = ITTokenizer("peaks.bed") -# if you want to see the training progress -logging.basicConfig(level=logging.INFO) +tokens = tokenizer(adata) -model = SCEmbed( - use_default_region_names=False # this is to specify that we want to use chr, start, end. -) -model.train(adata, epochs=3) # we recomend increasing this to 100 +for i, t in enumerate(tokens): + file = f"tokens{i}.gtok" + write_tokens_to_gtok(t, file) ``` -Thats it! - -## Clustering the cells -With the model now trained, we can get embeddings of our cells. This occurs in two steps: 1) tokenize the data and 2) encode the cells. +### Training the model -**Tokenize:** +Now that the data is pre-tokenized, we can train the model. The `scEmbed` model is designed to be used with `scanpy`. Here is an example of how to train the model: ```python -from geniml.tokenization import HardTokenizer +from geniml.region2vec.utils import Region2VecDataset -tokenizer = HardTokenizer("peaks.bed") # consensus peak set +dataset = Region2VecDataset("path/to/tokens") -region_sets = tokenizer(adata) +model = ScEmbed( + tokenizer=tokenizer, +) +model.train( + dataset, + epochs=100, +) ``` -**Encode:** +We can then export the model for upload to huggingface: + ```python -cell_embeddings = [] -for region_set in region_sets: - embedding = np.mean([model(r) for r in region_set if r in model.region2vec, axis=0]) - cell_embeddings.append(embedding) +model.export("path/to/model") +``` + +## Get embeddings of single-cells +`scEmbed` is simple to use and designed to be used with `scanpy`. Here is a simple example of how to train a model and get cell embeddings: -cell_embeddings = np.array(cell_embeddings) +```python +model = ScEmbed.from_pretrained("path/to/model") +model = ScEmbed("databio/scembed-pbmc10k") -adata.obsm['scembed_X'] = cell_embeddings +adata = sc.read_h5ad("path/to/adata.h5ad") +embeddings = model.encode(adata) + +adata.obsm["scembed_X"] = embeddings ``` -Now you can use `scanpy` utilities to cluster the cells: +## Clustering the cells +With the model now trained, and cell-embeddings obtained, we can get embeddings of our individual cells. You can use `scanpy` utilities to cluster the cells: ```python sc.pp.neighbors(adata, use_rep="scembed_X") -sc.tl.leiden(adata) +sc.tl.leiden(adata) # or louvain ``` And visualize with UMAP @@ -123,4 +120,4 @@ And visualize with UMAP ```python sc.tl.umap(adata) sc.pl.umap(adata, color="leiden") -``` \ No newline at end of file +``` diff --git a/docs/geniml/tutorials/use-pretrained-region2vec-model.md b/docs/geniml/tutorials/use-pretrained-region2vec-model.md index 877e328..e82c55b 100644 --- a/docs/geniml/tutorials/use-pretrained-region2vec-model.md +++ b/docs/geniml/tutorials/use-pretrained-region2vec-model.md @@ -8,7 +8,7 @@ To use one of our pre-trained models, simply import the `Region2VecExModel` and from geniml.io import Region from geniml.region2vec import Region2VecExModel -model = Region2VecExModel("databio/r2v-ChIP-atlas-hg38-v2") +model = Region2VecExModel("databio/r2v-encode-hg38") ``` > Note: We use the `Region2VecExModel` class to load the model because it is an extension of the `Region2Vec` class that comes with its own tokenizer. This ensures that the model and tokenizer are compatible. @@ -20,8 +20,8 @@ Now we can encode a genomic region or a list of regions: region = Region("chr1", 160100, 160200) regions = [region] * 10 -embedding = model.encode(region) -embeddings = model.encode(regions) +embedding = model.encode(region) # get one region embedding +embeddings = model.encode(regions) # or, get many embeddings ``` We can also encode an entire bed file, which will return region embeddings for each region in the file: @@ -32,24 +32,14 @@ bed = "/path/to/bed/file.bed" embeddings = model.encode(bed) ``` -> Note: It is not uncommon for a region to not be tokenizable by the tokenizer. This is because the tokenizer was trained on a specific set of regions. If this is the case, the model simply returns `None` for the embedding of that region. If you want to override this behavior, you can set `return_none=False` in the `encode` function. This will skip that region in the return. However, we do not recommend this as it removes the ability to distinguish between regions that are tokenizable and regions that are not. The output shape will no longer match the input shape. +> Note: It is possible that a region can not be tokenized by the tokenizer. This is because the tokenizer was instantiated with a specific set of regions. If this is the case, the model simply returns the unknown token (`chrUNK-0:0`). If you find that this is happening often, you may want to ensure that your regions are a good fit for the universe of regions that the model was trained on. The unknown token will indeed have an embedding, but it will not be a meaningful representation of the region. ## Region pooling -It is often the case that we want a single embedding that represents a set of regions. For example, we may want to encode a patient by taking the average embedding of all the SNPs in the patient's genome. We can do this by using the `pool` argument. Out of the box, we support `mean` and `max` pooling. For example: +It is often the case that we want a single embedding that represents a set of regions. For example, we may want to encode a patient by taking the average embedding of all the SNPs in the patient's genome. We can do this by simply averaging across the embeddings of the regions: ```python patient_snps = "/path/to/bed/file.bed" -embedding = model.encode(patient_snps, pool="mean") # or pool="max" -``` - -You can also supply a custom function to do pooling. For example, if you wanted to take the sum of the embeddings, you could do: - -```python -def sum_pooling(embeddings: np.ndarray): - return np.sum(embeddings, axis=0) - -embedding = model.encode(patient_snps, pool=sum_pooling) -``` - -`pool` is `False` by default. Setting to `True` defaults to `mean` pooling. +embeddings = model.encode(patient_snps) +patient_embedding = np.mean(embeddings, axis=0) +``` \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 7b899f0..e635b92 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -76,6 +76,8 @@ nav: - Evaluate embeddings: geniml/tutorials/evaluation.md - Train region2vec embeddings: geniml/tutorials/region2vec.md - Train single-cell embeddings: geniml/tutorials/train-scembed-model.md + - Load vector database with embeddings: tutorials/load-qdrant-with-cell-embeddings.md + - Cell-type prediction using KNN: tutorials/cell-type-annotation-with-knn.md - Tokenization: geniml/tutorials/tokenization.md - Tokenize a BED file on the command line: geniml/tutorials/cli-tokenization.md - Create consensus peaks: geniml/tutorials/create-consensus-peaks.md From 65091e6cd9e727952c33dd34d1f3f9f0ab07f8ed Mon Sep 17 00:00:00 2001 From: nsheff Date: Mon, 19 Feb 2024 21:04:49 -0500 Subject: [PATCH 05/17] clean up tutorials, geniml docs, etc --- autodoc.py | 102 ++++ docs/README.md | 9 +- .../code/config.md} | 45 +- .../bbconf_demo.md => bbconf/code/demo.md} | 79 +-- docs/bbconf/{ => notebooks}/config.ipynb | 0 docs/bbconf/{ => notebooks}/demo.ipynb | 0 .../code/bedbase-tutorial.md} | 0 .../code/bedmaker-tutorial.md} | 0 .../code/bedqc-tutorial.md} | 0 .../code/bedstat-tutorial.md} | 0 .../code/tutorial-all.md} | 0 .../bedboss/notebooks/bedbase-tutorial.ipynb | 0 .../bedboss/notebooks/bedmaker-tutorial.ipynb | 0 .../bedboss/notebooks/bedqc-tutorial.ipynb | 0 .../bedboss/notebooks/bedstat-tutorial.ipynb | 0 .../bedboss/notebooks/tutorial-all.ipynb | 0 docs/bedhost/README.md | 4 +- docs/bedhost/about.md | 6 - docs/citations.md | 4 + docs/geniml/README.md | 19 +- docs/geniml/contributing.md | 2 +- docs/geniml/img/geniml_logo.svg | 507 ++++++++++++++-- docs/geniml/img/geniml_logo1.svg | 63 ++ docs/geniml/img/geniml_logo_horizontal.svg | 255 ++++++++ docs/geniml/tutorials/assess-universe.md | 2 +- docs/geniml/tutorials/bedspace.md | 21 +- docs/stylesheets/extra.css | 9 + docs_jupyter/bbconf_config.ipynb | 149 ----- docs_jupyter/bbconf_demo.ipynb | 552 ------------------ docs_jupyter/build/.gitignore | 2 - mkdocs.yml | 41 +- 31 files changed, 1003 insertions(+), 868 deletions(-) create mode 100644 autodoc.py rename docs/{autodoc_build/bbconf_config.md => bbconf/code/config.md} (72%) rename docs/{autodoc_build/bbconf_demo.md => bbconf/code/demo.md} (85%) rename docs/bbconf/{ => notebooks}/config.ipynb (100%) rename docs/bbconf/{ => notebooks}/demo.ipynb (100%) rename docs/{autodoc_build/bedbase_tutorial.md => bedboss/code/bedbase-tutorial.md} (100%) rename docs/{autodoc_build/bedmaker_tutorial.md => bedboss/code/bedmaker-tutorial.md} (100%) rename docs/{autodoc_build/bedqc_tutorial.md => bedboss/code/bedqc-tutorial.md} (100%) rename docs/{autodoc_build/bedstat_tutorial.md => bedboss/code/bedstat-tutorial.md} (100%) rename docs/{autodoc_build/tutorial_all.md => bedboss/code/tutorial-all.md} (100%) rename docs_jupyter/bedbase_tutorial.ipynb => docs/bedboss/notebooks/bedbase-tutorial.ipynb (100%) rename docs_jupyter/bedmaker_tutorial.ipynb => docs/bedboss/notebooks/bedmaker-tutorial.ipynb (100%) rename docs_jupyter/bedqc_tutorial.ipynb => docs/bedboss/notebooks/bedqc-tutorial.ipynb (100%) rename docs_jupyter/bedstat_tutorial.ipynb => docs/bedboss/notebooks/bedstat-tutorial.ipynb (100%) rename docs_jupyter/tutorial_all.ipynb => docs/bedboss/notebooks/tutorial-all.ipynb (100%) delete mode 100644 docs/bedhost/about.md create mode 100644 docs/geniml/img/geniml_logo1.svg create mode 100644 docs/geniml/img/geniml_logo_horizontal.svg delete mode 100644 docs_jupyter/bbconf_config.ipynb delete mode 100644 docs_jupyter/bbconf_demo.ipynb delete mode 100644 docs_jupyter/build/.gitignore diff --git a/autodoc.py b/autodoc.py new file mode 100644 index 0000000..e5c7aa1 --- /dev/null +++ b/autodoc.py @@ -0,0 +1,102 @@ +# This script will auto-generate documentation for Python code, CLI usage, and Jupyter notebooks +# It is intended to be run as a pre-build step in a MkDocs project +# It will read the mkdocs.yml file for configuration +# It will use the lucidoc package to auto-generate documentation for Python code +# It will use the subprocess package to run CLI commands and capture the output +# It will use the nbconvert package to convert Jupyter notebooks to markdown + +import lucidoc +import yaml +import subprocess +import glob +import nbconvert +import os +from pathlib import Path + +import argparse + +parser = argparse.ArgumentParser(description="Description of your program") +parser.add_argument( + "--x-usage", + help="Exclude usage", + required=False, + default=False, + action="store_true", +) +parser.add_argument( + "--x-lucidoc", + help="Exclude lucidoc", + required=False, + default=False, + action="store_true", +) +parser.add_argument( + "--x-jupyter", + help="Exclude jupyter", + required=False, + default=False, + action="store_true", +) + +args = vars(parser.parse_args()) + +print(args) + +# Read the mkdocs config +with open("mkdocs.yml") as stream: + cfg = yaml.safe_load(stream) + + +if "autodoc" not in cfg: + print("No autodoc configuration found in mkdocs.yml") + exit(1) +else: + cfg = cfg["autodoc"] + +# Process auto-documented Python code +if args["x_lucidoc"] is False and "lucidoc" in cfg: + for bundle in cfg["lucidoc"]: + print(f"Documenting lucidoc '{bundle['pkg']}' at {bundle['outfile']}") + lucidoc.run_lucidoc(parse_style="rst", **bundle) +else: + print("Skipping lucidoc") + + +usage_tpl = """ +\n`{cmd}` +\n +```console +{usage} +``` +""" + +# Process CLI usage +if args["x_usage"] is False and "cli_usage" in cfg: + for item in cfg["cli_usage"]: + result = "" + with open(item["template"], "r") as file: + result = file.read() + for cmd in item["commands"]: + print(f"Documenting command '{cmd}' to '{item['outfile']}'") + usage = subprocess.check_output(cmd, shell=True).decode("utf-8") + content = usage_tpl.format(cmd=cmd, usage=usage) + result += content + with open(item["outfile"], "w") as file: + file.write(result) +else: + print("Skipping usage documentation") + +# # Render Juptyer notebooks to markdown +if args["x_jupyter"] is False and "jupyter" in cfg: + for item in cfg["jupyter"]: + files = glob.glob(f"docs/{item['in']}/*.ipynb") + for nb in files: + bn, _ = os.path.splitext(os.path.basename(nb)) + out = f"docs/{item['out']}/{bn}.md" + print(f"Converting '{nb}' to '{out}'") + md_result = nbconvert.exporters.export(nbconvert.MarkdownExporter(), nb)[0] + Path(os.path.dirname(out)).mkdir(parents=True, exist_ok=True) + with open(out, "w") as stream: + stream.write(md_result) +else: + print("Skipping jupyter notebooks") \ No newline at end of file diff --git a/docs/README.md b/docs/README.md index 842ba3f..bf5ef5d 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,5 +1,12 @@ +--- +hide: + - navigation + - toc + - navigation.footer +--- + # Welcome to -This site hosts developer and user documentation for components of BEDbase and related tools. Use the tab navigation above to find the project of interest. +This site hosts developer and user documentation for components of BEDbase and related tools, notably including [geniml](geniml/README.md), our package for machine learning on genomic intervals. Use the tab navigation above to find the project of interest. You can access the main BEDbase interface at . \ No newline at end of file diff --git a/docs/autodoc_build/bbconf_config.md b/docs/bbconf/code/config.md similarity index 72% rename from docs/autodoc_build/bbconf_config.md rename to docs/bbconf/code/config.md index 1890e4c..ea8e269 100644 --- a/docs/autodoc_build/bbconf_config.md +++ b/docs/bbconf/code/config.md @@ -1,4 +1,3 @@ -jupyter:True # Bedbase configuration file documentation In order to start working with the `BedBaseConf` object, it has to be initialized first. The constuctor requires one argument, which is a path to the configuration file (in YAML format). @@ -18,14 +17,12 @@ Here's an example of a minimal bedbase configuration file: !cat ../tests/data/config_min.yaml ``` -```.output -# min config example. Refer to bbconf/const.py for key names and default values - -path: - pipeline_output_path: $HOME/bedbase - bedstat_dir: bedstat_output - bedbuncher_dir: bedbuncher_output -``` + # min config example. Refer to bbconf/const.py for key names and default values + + path: + pipeline_output_path: $HOME/bedbase + bedstat_dir: bedstat_output + bedbuncher_dir: bedbuncher_output ## Example config file @@ -38,22 +35,20 @@ Here's an example of a complete bedbase configuration file: !cat ../tests/data/config.yaml ``` -```.output -database: - name: pipestat-test - user: postgres - password: pipestat-password - host: localhost -# port: 5432; intentionally commented out to test the defaults setting system -path: - pipeline_output_path: $BEDBASE_DATA_PATH/outputs - bedstat_dir: bedstat_output - bedbuncher_dir: bedbuncher_output - remote_url_base: null -server: - host: 0.0.0.0 - port: 8000 -``` + database: + name: pipestat-test + user: postgres + password: pipestat-password + host: localhost + # port: 5432; intentionally commented out to test the defaults setting system + path: + pipeline_output_path: $BEDBASE_DATA_PATH/outputs + bedstat_dir: bedstat_output + bedbuncher_dir: bedbuncher_output + remote_url_base: null + server: + host: 0.0.0.0 + port: 8000 ## Default values diff --git a/docs/autodoc_build/bbconf_demo.md b/docs/bbconf/code/demo.md similarity index 85% rename from docs/autodoc_build/bbconf_demo.md rename to docs/bbconf/code/demo.md index 48ab2bf..c4dcfff 100644 --- a/docs/autodoc_build/bbconf_demo.md +++ b/docs/bbconf/code/demo.md @@ -1,4 +1,3 @@ -jupyter:True # `BedBaseConf` object usage demonstration `bbconf` standardizes reporting of [bedstat](https://github.com/databio/bedstat) and [bedbuncher](https://github.com/databio/bedsbuncher) results. It formalizes a way for these pipelines and downstream tools communicate -- the produced results can easily and reliably become an @@ -16,10 +15,8 @@ from bbconf import * bbc = BedBaseConf(config_path="../tests/data/config.yaml") ``` -```.output -DEBU 10:09:08 | bbconf:est:266 > Configured logger 'bbconf' using logmuse v0.2.6 + DEBU 10:09:08 | bbconf:est:266 > Configured logger 'bbconf' using logmuse v0.2.6 -``` As you can see above, missing entries are populated with default values. @@ -36,14 +33,12 @@ The `PipestatManager` instances for bedfiles and bedsets can be accessed via the print(bbc.bed) ``` -```.output -PipestatManager (bedfiles) -Backend: PostgreSQL -Results schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/bbconf/schemas/bedfiles_schema.yaml -Status schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/schemas/status_schema.yaml -Records count: 11 + PipestatManager (bedfiles) + Backend: PostgreSQL + Results schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/bbconf/schemas/bedfiles_schema.yaml + Status schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/schemas/status_schema.yaml + Records count: 11 -``` ### `BedBaseConf.bedset`: @@ -52,14 +47,12 @@ Records count: 11 print(bbc.bedset) ``` -```.output -PipestatManager (bedsets) -Backend: PostgreSQL -Results schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/bbconf/schemas/bedsets_schema.yaml -Status schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/schemas/status_schema.yaml -Records count: 3 + PipestatManager (bedsets) + Backend: PostgreSQL + Results schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/bbconf/schemas/bedsets_schema.yaml + Status schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/schemas/status_schema.yaml + Records count: 3 -``` ### `BedBaseConf.config`: @@ -70,23 +63,21 @@ Additionally, there's a `BedBaseConf.config` property, that can be used to retri print(bbc.config) ``` -```.output -database: - name: pipestat-test - user: postgres - password: pipestat-password - host: localhost - port: 5432 -path: - pipeline_output_path: $BEDBASE_DATA_PATH/outputs - bedstat_dir: bedstat_output - bedbuncher_dir: bedbuncher_output - remote_url_base: null -server: - host: 0.0.0.0 - port: 8000 + database: + name: pipestat-test + user: postgres + password: pipestat-password + host: localhost + port: 5432 + path: + pipeline_output_path: $BEDBASE_DATA_PATH/outputs + bedstat_dir: bedstat_output + bedbuncher_dir: bedbuncher_output + remote_url_base: null + server: + host: 0.0.0.0 + port: 8000 -``` ## Running a database @@ -108,11 +99,9 @@ print(bbc.bed.schema["name"]) print(bbc.bed.schema["widths_histogram"]) ``` -```.output -{'type': 'string', 'description': 'BED file name'} -{'type': 'image', 'description': 'Quantile-trimmed histogram of widths'} + {'type': 'string', 'description': 'BED file name'} + {'type': 'image', 'description': 'Quantile-trimmed histogram of widths'} -``` A result of type `image` is in fact a mapping with three required elements: `path`, `thumbnail_path` and `title`. The actual jsonschema schemas can be accessed as `result_schemas` property for both tables: @@ -158,10 +147,8 @@ bbc.bed.record_count bbc.bed.report(record_identifier="78c0e4753d04b238fc07e4ebe5a02984", values={"name": "some_name"}) ``` -```.output -These results exist for '78c0e4753d04b238fc07e4ebe5a02984': ['name'] + These results exist for '78c0e4753d04b238fc07e4ebe5a02984': ['name'] -``` @@ -216,11 +203,9 @@ Let's try reporting a new bedfile then: bbc.bed.report(record_identifier="78c1e4111d04b238fc11e4ebe5a02984", values={"name": "some_name"}) ``` -```.output -Reported records for '78c1e4111d04b238fc11e4ebe5a02984' in 'bedfiles' namespace: - - name: some_name + Reported records for '78c1e4111d04b238fc11e4ebe5a02984' in 'bedfiles' namespace: + - name: some_name -``` @@ -266,10 +251,8 @@ Naturally, a record can be removed: bbc.bed.remove(record_identifier="78c1e4111d04b238fc11e4ebe5a02984") ``` -```.output -Removing '78c1e4111d04b238fc11e4ebe5a02984' record + Removing '78c1e4111d04b238fc11e4ebe5a02984' record -``` diff --git a/docs/bbconf/config.ipynb b/docs/bbconf/notebooks/config.ipynb similarity index 100% rename from docs/bbconf/config.ipynb rename to docs/bbconf/notebooks/config.ipynb diff --git a/docs/bbconf/demo.ipynb b/docs/bbconf/notebooks/demo.ipynb similarity index 100% rename from docs/bbconf/demo.ipynb rename to docs/bbconf/notebooks/demo.ipynb diff --git a/docs/autodoc_build/bedbase_tutorial.md b/docs/bedboss/code/bedbase-tutorial.md similarity index 100% rename from docs/autodoc_build/bedbase_tutorial.md rename to docs/bedboss/code/bedbase-tutorial.md diff --git a/docs/autodoc_build/bedmaker_tutorial.md b/docs/bedboss/code/bedmaker-tutorial.md similarity index 100% rename from docs/autodoc_build/bedmaker_tutorial.md rename to docs/bedboss/code/bedmaker-tutorial.md diff --git a/docs/autodoc_build/bedqc_tutorial.md b/docs/bedboss/code/bedqc-tutorial.md similarity index 100% rename from docs/autodoc_build/bedqc_tutorial.md rename to docs/bedboss/code/bedqc-tutorial.md diff --git a/docs/autodoc_build/bedstat_tutorial.md b/docs/bedboss/code/bedstat-tutorial.md similarity index 100% rename from docs/autodoc_build/bedstat_tutorial.md rename to docs/bedboss/code/bedstat-tutorial.md diff --git a/docs/autodoc_build/tutorial_all.md b/docs/bedboss/code/tutorial-all.md similarity index 100% rename from docs/autodoc_build/tutorial_all.md rename to docs/bedboss/code/tutorial-all.md diff --git a/docs_jupyter/bedbase_tutorial.ipynb b/docs/bedboss/notebooks/bedbase-tutorial.ipynb similarity index 100% rename from docs_jupyter/bedbase_tutorial.ipynb rename to docs/bedboss/notebooks/bedbase-tutorial.ipynb diff --git a/docs_jupyter/bedmaker_tutorial.ipynb b/docs/bedboss/notebooks/bedmaker-tutorial.ipynb similarity index 100% rename from docs_jupyter/bedmaker_tutorial.ipynb rename to docs/bedboss/notebooks/bedmaker-tutorial.ipynb diff --git a/docs_jupyter/bedqc_tutorial.ipynb b/docs/bedboss/notebooks/bedqc-tutorial.ipynb similarity index 100% rename from docs_jupyter/bedqc_tutorial.ipynb rename to docs/bedboss/notebooks/bedqc-tutorial.ipynb diff --git a/docs_jupyter/bedstat_tutorial.ipynb b/docs/bedboss/notebooks/bedstat-tutorial.ipynb similarity index 100% rename from docs_jupyter/bedstat_tutorial.ipynb rename to docs/bedboss/notebooks/bedstat-tutorial.ipynb diff --git a/docs_jupyter/tutorial_all.ipynb b/docs/bedboss/notebooks/tutorial-all.ipynb similarity index 100% rename from docs_jupyter/tutorial_all.ipynb rename to docs/bedboss/notebooks/tutorial-all.ipynb diff --git a/docs/bedhost/README.md b/docs/bedhost/README.md index 0f24129..a6a53bf 100644 --- a/docs/bedhost/README.md +++ b/docs/bedhost/README.md @@ -31,10 +31,10 @@ Object IDs take the form `..`. An exa So, you can get information about this object like this: -`GET` [/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile](/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile) +`GET` [https://api.bedbase.org/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile](https://api.bedbase.org/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile) Or, you can get a URL to download the actual file with: -`GET` [/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http](/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http) +`GET` [https://api.bedbase.org/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http](https://api.bedbase.org/objects/bed.eaf9ee97241f300f1c7e76e1f945141f.bedfile/access/http) diff --git a/docs/bedhost/about.md b/docs/bedhost/about.md deleted file mode 100644 index 529dfcf..0000000 --- a/docs/bedhost/about.md +++ /dev/null @@ -1,6 +0,0 @@ -# about - -testing about - -[here is a link](/docs) - diff --git a/docs/citations.md b/docs/citations.md index 122be5b..937674f 100644 --- a/docs/citations.md +++ b/docs/citations.md @@ -13,6 +13,10 @@ Thanks for citing us! If you use BEDbase, geniml, or their components in your re |---------------|-----------------| | `geniml` region set evaluations | Zheng et al. (2023) *bioRxiv* | | `region2vec` embeddings | Gharavi et al. (2021) *Bioinformatics* | +| `bedspace` search and embeddings | Gharavi et al. (2023) *bioRxiv* | +| `geniml hmm` module | Rymuza et al. (2023) *bioRxiv* | +| `bedbase` database | Unpublished | +| `scEmbed` single-cell embedding framework | LeRoy et al. (2023) *bioRxiv* | diff --git a/docs/geniml/README.md b/docs/geniml/README.md index 8ff1d8d..d38773c 100644 --- a/docs/geniml/README.md +++ b/docs/geniml/README.md @@ -1,19 +1,24 @@ -# Genomic interval toolkit +# + +

+ + +

+ + ## Introduction -Geniml is a python package for building machine learning models of genomic interval data (BED files). It also includes ancillary functions to support other types of analyses of genomic interval data. +Geniml is a *genomic interval machine learning toolkit*, a Python package for building machine learning models of genomic interval data (BED files). It also includes ancillary functions to support other types of analyses of genomic interval data. + +As of Feburary 2024, this package and its documentation are undergoing rapid development, leading to some tutorials getting outdated. Please raise [github issues](https://github.com/databio/geniml) if you find outdated or unclear directions, so we know where to focus effort that will benefit users. ## Install ``` -pip install --user --upgrade . +pip install --user --upgrade geniml ``` ## Modules `geniml` is organized into modules. The next section is an overview of each module. You can also proceed to the how-to guides for recipes on how to do specfic tasks. - -## Citing - -If you find `geniml` useful for your research, please cite us! It helps us convince others that our work is useful. You can find a [published papers describing geniml components](manuscripts.md). \ No newline at end of file diff --git a/docs/geniml/contributing.md b/docs/geniml/contributing.md index 2ae9044..dfee014 100644 --- a/docs/geniml/contributing.md +++ b/docs/geniml/contributing.md @@ -56,5 +56,5 @@ geniml.hmm.function() ### Shared code -Any variables, functions, or other code that is shared across modules should be placed in the parent module, which is held in the [geniml](geniml) folder. +Any variables, functions, or other code that is shared across modules should be placed in the parent module, which is held in the geniml folder. diff --git a/docs/geniml/img/geniml_logo.svg b/docs/geniml/img/geniml_logo.svg index 7aeb1b5..376af38 100644 --- a/docs/geniml/img/geniml_logo.svg +++ b/docs/geniml/img/geniml_logo.svg @@ -1,63 +1,474 @@ + + + style="stroke-width:0.0306422" />
diff --git a/docs/geniml/img/geniml_logo1.svg b/docs/geniml/img/geniml_logo1.svg new file mode 100644 index 0000000..7aeb1b5 --- /dev/null +++ b/docs/geniml/img/geniml_logo1.svg @@ -0,0 +1,63 @@ + + diff --git a/docs/geniml/img/geniml_logo_horizontal.svg b/docs/geniml/img/geniml_logo_horizontal.svg new file mode 100644 index 0000000..3381c23 --- /dev/null +++ b/docs/geniml/img/geniml_logo_horizontal.svg @@ -0,0 +1,255 @@ + + + + diff --git a/docs/geniml/tutorials/assess-universe.md b/docs/geniml/tutorials/assess-universe.md index 2cd6045..ac50fb7 100644 --- a/docs/geniml/tutorials/assess-universe.md +++ b/docs/geniml/tutorials/assess-universe.md @@ -92,7 +92,7 @@ closeness_score = get_closeness_score("tests/consensus/raw", ## Universe likelihood We can also calculate the likelihood of universe given collection of file. For that we -will need [likelihood model](consensus-peaks.md#making-likelihood-model-). We can do it +will need [likelihood model](create-consensus-peaks.md#making-likelihood-model-). We can do it either for hard universe: ``` diff --git a/docs/geniml/tutorials/bedspace.md b/docs/geniml/tutorials/bedspace.md index be78bb1..de58daf 100644 --- a/docs/geniml/tutorials/bedspace.md +++ b/docs/geniml/tutorials/bedspace.md @@ -2,14 +2,25 @@ ## Introduction -To ensure that everything is working correctly, run: `python -c "from geniml import bedspace"`. There are four main commands in `bedspace`: +BEDspace is an application of the StarSpace model to genomic interval data, described in [Gharavi et al. 2023](../../citations.md). It allows us to train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, BEDspace solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string; suggesting new labels for database region sets; and retrieving database region sets similar to a query region set. + +## Installation + +The `bedspace` module is installed with `geniml`. To ensure that everything is working correctly, run: `python -c "from geniml import bedspace"`. + +## BEDspace operations + +There are four main commands in `bedspace`: 1. `bedspace preprocess`: preprocesses a set of genomic interval regions and their associated metadata into a format that can be used by `bedspace train`. 2. `bedspace train`: trains a StarSpace model on the preprocessed data. 3. `bedspace distances`: computes distances between region sets in the trained model and metadata labels. 4. `bedspace search`: searches for the most similar region sets and metadata labels to a given query. Three scenarios for this command are described in the details. +These commands are accessed via the command line with `genimtools bedspace `. + ### `bedspace preprocess` + The `preprocess` command will prepare a set of region sets and metadata labels for training. This includes things like adding the `__label__` prefix to metadata labels, and converting the region sets into a format that can be used by StarSpace. The command takes in a set of region sets and metadata labels, and outputs a set of preprocessed region sets and metadata labels. The command can be run as follows: ```console @@ -30,6 +41,7 @@ Input Description: ### `bedspace train` + The `train` command will train a StarSpace model on the preprocessed region sets and metadata labels. It requires that you have run the `preprocess` command first. The `train` command takes in a set of preprocessed region sets and metadata labels, and outputs a trained StarSpace model. The command can be run as follows: ```console @@ -51,12 +63,8 @@ Input Description: `--epochs`: Specifies the number of epochs to train the StartSpace model. `--lr`: Sets the learning rate for the training process. - - - - - ### `bedspace distances` + The `distances` command will compute the distances between all of the region sets and metadata labels in the trained model. It requires that you have ran the `train` command first. The `distances` command takes in a trained StarSpace model, and outputs a set of distances between all of the region sets and metadata labels in the model. The command can be run as follows: ```console @@ -117,7 +125,6 @@ geniml bedspace search \ path/to/regions.bed ``` - Input Description: `-t`: Specifies the search type. diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 4ee90a5..2649237 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -35,6 +35,15 @@ text-align: center; } +.img-header { + text-align: center; + display: block; + margin-left: auto; + margin-right: auto; + width: 50%; +} + + .jumbotron { padding-top: 30px; padding-bottom: 30px; diff --git a/docs_jupyter/bbconf_config.ipynb b/docs_jupyter/bbconf_config.ipynb deleted file mode 100644 index 7e285d3..0000000 --- a/docs_jupyter/bbconf_config.ipynb +++ /dev/null @@ -1,149 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bedbase configuration file documentation\n", - "\n", - "In order to start working with the `BedBaseConf` object, it has to be initialized first. The constuctor requires one argument, which is a path to the configuration file (in YAML format).\n", - "\n", - "## Minimal config file\n", - "\n", - "The minimal configuration must define the `path` section with 3 keys:\n", - "\n", - "- `pipeline_output_path`: path to the desired output directory for the pipelines\n", - "- `bedstat_dir`: name of the [bedstat](https://github.com/databio/bedstat) pipeline output directory\n", - "- `bedbuncher_dir`: name of the [bedbuncher](https://github.com/databio/bedbuncher) pipeline output directory\n", - "\n", - "Here's an example of a minimal bedbase configuration file:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# min config example. Refer to bbconf/const.py for key names and default values\r\n", - "\r\n", - "path:\r\n", - " pipeline_output_path: $HOME/bedbase\r\n", - " bedstat_dir: bedstat_output\r\n", - " bedbuncher_dir: bedbuncher_output" - ] - } - ], - "source": [ - "!cat ../tests/data/config_min.yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example config file\n", - "\n", - "Apart from the required `path` section, there are 2 other sections that can be used to configure the PostgreSQL database, used to store the metadata about the bedfiles and bedsets (`database` section) and to configure the bedhost server that displays the pipeline results and provides an API to query them (`server` section).\n", - "\n", - "Here's an example of a complete bedbase configuration file:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "database:\r\n", - " name: pipestat-test\r\n", - " user: postgres\r\n", - " password: pipestat-password\r\n", - " host: localhost\r\n", - "# port: 5432; intentionally commented out to test the defaults setting system\r\n", - "path:\r\n", - " pipeline_output_path: $BEDBASE_DATA_PATH/outputs\r\n", - " bedstat_dir: bedstat_output\r\n", - " bedbuncher_dir: bedbuncher_output\r\n", - " remote_url_base: null\r\n", - "server:\r\n", - " host: 0.0.0.0\r\n", - " port: 8000" - ] - } - ], - "source": [ - "!cat ../tests/data/config.yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Default values\n", - "\n", - "In case any of the values shown below is not provided in the configuration file, it will be set to a default value" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AttMap\n", - "path:\n", - " remote_url_base: null\n", - "database:\n", - " user: postgres\n", - " password: bedbasepassword\n", - " name: postgres\n", - " port: 5432\n", - " host: localhost\n", - "server:\n", - " host: 0.0.0.0\n", - " port: 80" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bbconf.const import DEFAULT_SECTION_VALUES\n", - "from attmap import AttMap\n", - "AttMap(DEFAULT_SECTION_VALUES)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs_jupyter/bbconf_demo.ipynb b/docs_jupyter/bbconf_demo.ipynb deleted file mode 100644 index 634cc11..0000000 --- a/docs_jupyter/bbconf_demo.ipynb +++ /dev/null @@ -1,552 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `BedBaseConf` object usage demonstration\n", - "\n", - "`bbconf` standardizes reporting of [bedstat](https://github.com/databio/bedstat) and [bedbuncher](https://github.com/databio/bedsbuncher) results. It formalizes a way for these pipelines and downstream tools communicate -- the produced results can easily and reliably become an\n", - "input for the server ([bedhost](https://github.com/databio/bedhost)). The object exposes API for interacting with the results and is backed by a [PostgreSQL](https://www.postgresql.org/) database.\n", - "\n", - "\n", - "`bbconf` provides a way to easily determine a path to the required configuration file. The file can be pointed to by the `$BEDBASE` environment variable. `get_bedbase_cfg` function returns a path which can be either excplicitly provided as an argument or read from the environment variable." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBU 10:09:08 | bbconf:est:266 > Configured logger 'bbconf' using logmuse v0.2.6 \n" - ] - } - ], - "source": [ - "import logmuse\n", - "logmuse.init_logger(\"bbconf\", \"DEBUG\")\n", - "from bbconf import *\n", - "\n", - "bbc = BedBaseConf(config_path=\"../tests/data/config.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see above, missing entries are populated with default values.\n", - "\n", - "## Object contents" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`BedBaseConf` objects consist of two [`PipestatManager`](http://pipestat.databio.org/) instances. These objects are responsible for bedfiles and bedsets metadata management. Additionally, `BedBaseConf` maintains a \"relationship table\" that stores the information regarding the bedfile-bedset relationsips, i.e. which bedfile is a part of which bedset.\n", - "\n", - "The `PipestatManager` instances for bedfiles and bedsets can be accessed via the object properties: `BedBaseConf.bed` and `BedBaseConf.bedset`, respectively:\n", - "\n", - "### `BedBaseConf.bed`:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PipestatManager (bedfiles)\n", - "Backend: PostgreSQL\n", - "Results schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/bbconf/schemas/bedfiles_schema.yaml\n", - "Status schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/schemas/status_schema.yaml\n", - "Records count: 11\n" - ] - } - ], - "source": [ - "print(bbc.bed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `BedBaseConf.bedset`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PipestatManager (bedsets)\n", - "Backend: PostgreSQL\n", - "Results schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/bbconf/schemas/bedsets_schema.yaml\n", - "Status schema source: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/schemas/status_schema.yaml\n", - "Records count: 3\n" - ] - } - ], - "source": [ - "print(bbc.bedset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `BedBaseConf.config`:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, there's a `BedBaseConf.config` property, that can be used to retrieve the bedbase project configuration values, which include both ones declared in the configuration file and default ones:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "database:\n", - " name: pipestat-test\n", - " user: postgres\n", - " password: pipestat-password\n", - " host: localhost\n", - " port: 5432\n", - "path:\n", - " pipeline_output_path: $BEDBASE_DATA_PATH/outputs\n", - " bedstat_dir: bedstat_output\n", - " bedbuncher_dir: bedbuncher_output\n", - " remote_url_base: null\n", - "server:\n", - " host: 0.0.0.0\n", - " port: 8000\n" - ] - } - ], - "source": [ - "print(bbc.config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running a database" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we start interacting with the database, we need to establish the connection. The required database information is sourced from the object itself. Obviously, the PostgreSQL database instance has to be launched before and running in the background. For example, to run the database in a Docker container, execute these two lines:\n", - "\n", - "```\n", - "docker volume create postgres-data\n", - "docker run -d --name bedbase-postgres -p 5432:5432 -e POSTGRES_PASSWORD=bedbasepassword -e POSTGRES_USER=postgres -e POSTGRES_DB=postgres -v postgres-data:/var/lib/postgresql/data postgres\n", - "```\n", - "The environment variables passed to the container need to match the settings in `BedBaseConf` object." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Standardized metadata specification\n", - "\n", - "`bbconf` package comes with a predefined schemas, that describe the required bed and bedset metadata including the identifiers and types. For example, name of the bedfile, that will be stored in the column `\"name\"` has to be a string, whereas columns `\"widths_histogram\"` expects an image:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'type': 'string', 'description': 'BED file name'}\n", - "{'type': 'image', 'description': 'Quantile-trimmed histogram of widths'}\n" - ] - } - ], - "source": [ - "print(bbc.bed.schema[\"name\"])\n", - "print(bbc.bed.schema[\"widths_histogram\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A result of type `image` is in fact a mapping with three required elements: `path`, `thumbnail_path` and `title`. The actual jsonschema schemas can be accessed as `result_schemas` property for both tables:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'object',\n", - " 'description': 'Quantile-trimmed histogram of widths',\n", - " 'properties': {'path': {'type': 'string'},\n", - " 'thumbnail_path': {'type': 'string'},\n", - " 'title': {'type': 'string'}},\n", - " 'required': ['path', 'thumbnail_path', 'title']}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.bed.result_schemas[\"widths_histogram\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convenient metadata management and exploration\n", - "\n", - "Building on `PipestatManager`s `BedBaseConf` offers multiple methods for bedfile and bedset metadata management and exploration. Here are some examples:\n", - "\n", - "### Get the number of reported bedfiles" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.bed.record_count" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Report metadata for a bedfile or bedset" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "These results exist for '78c0e4753d04b238fc07e4ebe5a02984': ['name']\n" - ] - }, - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.bed.report(record_identifier=\"78c0e4753d04b238fc07e4ebe5a02984\", values={\"name\": \"some_name\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Oops, `name` for this bedfile has been reported already. `BedBaseConf`, does not allow reporting results overwriting, unless it's explicitly forced with `force_overwrite=True`.\n", - "\n", - "Let's try reporting a different value:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "'test' is not a known result. Results defined in the schema are: ['name', 'md5sum', 'bedfile', 'bigbedfile', 'regions_no', 'gc_content', 'mean_absolute_tss_dist', 'mean_region_width', 'exon_frequency', 'intron_frequency', 'promoterprox_frequency', 'intergenic_frequency', 'promotercore_frequency', 'fiveutr_frequency', 'threeutr_frequency', 'fiveutr_percentage', 'threeutr_percentage', 'promoterprox_percentage', 'exon_percentage', 'intron_percentage', 'intergenic_percentage', 'promotercore_percentage', 'tssdist', 'chrombins', 'gccontent', 'paritions', 'expected_partitions', 'cumulative_partitions', 'widths_histogram', 'neighbor_distances', 'open_chromatin', 'other'].", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbbc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord_identifier\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"78c0e4753d04b238fc07e4ebe5a02984\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"some_value\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/pipestat.py\u001b[0m in \u001b[0;36mreport\u001b[0;34m(self, values, record_identifier, force_overwrite, strict_type, return_id)\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mSchemaNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"report results\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[0mresult_identifiers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 766\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_results_defined\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_identifiers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 767\u001b[0m existing = self._check_which_results_exist(\n\u001b[1;32m 768\u001b[0m rid=record_identifier, results=result_identifiers)\n", - "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pipestat/pipestat.py\u001b[0m in \u001b[0;36massert_results_defined\u001b[0;34m(self, results)\u001b[0m\n\u001b[1;32m 1029\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1030\u001b[0m assert r in known_results, SchemaError(\n\u001b[0;32m-> 1031\u001b[0;31m \u001b[0;34mf\"'{r}' is not a known result. Results defined in the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1032\u001b[0m f\"schema are: {list(known_results)}.\")\n\u001b[1;32m 1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAssertionError\u001b[0m: 'test' is not a known result. Results defined in the schema are: ['name', 'md5sum', 'bedfile', 'bigbedfile', 'regions_no', 'gc_content', 'mean_absolute_tss_dist', 'mean_region_width', 'exon_frequency', 'intron_frequency', 'promoterprox_frequency', 'intergenic_frequency', 'promotercore_frequency', 'fiveutr_frequency', 'threeutr_frequency', 'fiveutr_percentage', 'threeutr_percentage', 'promoterprox_percentage', 'exon_percentage', 'intron_percentage', 'intergenic_percentage', 'promotercore_percentage', 'tssdist', 'chrombins', 'gccontent', 'paritions', 'expected_partitions', 'cumulative_partitions', 'widths_histogram', 'neighbor_distances', 'open_chromatin', 'other']." - ] - } - ], - "source": [ - "bbc.bed.report(record_identifier=\"78c0e4753d04b238fc07e4ebe5a02984\", values={\"test\": \"some_value\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Oops, the result `test` is not allowed, since it hasn't been specified in the schema. Results that are allowed are prinded in the error message above.\n", - "\n", - "Let's try reporting a new bedfile then:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Reported records for '78c1e4111d04b238fc11e4ebe5a02984' in 'bedfiles' namespace:\n", - " - name: some_name\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.bed.report(record_identifier=\"78c1e4111d04b238fc11e4ebe5a02984\", values={\"name\": \"some_name\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Success, the name for the bedfile identified by `78c1e4111d04b238fc11e4ebe5a02984` has been reported.\n", - "\n", - "Therefore, we can retrieve this result:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'some_name'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.bed.retrieve(record_identifier=\"78c1e4111d04b238fc11e4ebe5a02984\", result_identifier=\"name\") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or all the reported results:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'some_name'}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.bed.retrieve(record_identifier=\"78c1e4111d04b238fc11e4ebe5a02984\") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Naturally, a record can be removed:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Removing '78c1e4111d04b238fc11e4ebe5a02984' record\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.bed.remove(record_identifier=\"78c1e4111d04b238fc11e4ebe5a02984\") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Report bedfile-bedset relationships" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another useful feature of `BedBaseConf` is conveninent many to many bedfile-bedset relationships handling. To report one use `BedBaseConf.report_relationship` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "bbc.report_relationship(bedfile_id=3, bedset_id=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can select bedfiles that are part of a bedsets with name \"bedsetOver1kRegions\". Therefore they need to match the following query: `name='bedsetOver1kRegions'`. With `bedfile_col` argument we select the bedfile table columns we're interested in:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1, 'GSE105587_ENCFF018NNF_conservative_idr_thresholded_peaks_GRCh38'],\n", - " [2, 'GSE105977_ENCFF617QGK_optimal_idr_thresholded_peaks_GRCh38'],\n", - " [3, 'GSE105977_ENCFF793SZW_conservative_idr_thresholded_peaks_GRCh38'],\n", - " [4, 'GSE105977_ENCFF937CGY_peaks_GRCh38'],\n", - " [5, 'GSE91663_ENCFF316ASR_peaks_GRCh38'],\n", - " [6, 'GSE91663_ENCFF319TPR_conservative_idr_thresholded_peaks_GRCh38'],\n", - " [7, 'GSE91663_ENCFF553KIK_optimal_idr_thresholded_peaks_GRCh38'],\n", - " [8, 'GSM2423312_ENCFF155HVK_peaks_GRCh38'],\n", - " [9, 'GSM2423313_ENCFF722AOG_peaks_GRCh38'],\n", - " [10, 'GSM2827349_ENCFF196DNQ_peaks_GRCh38'],\n", - " [11, 'GSM2827350_ENCFF928JXU_peaks_GRCh38']]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bbc.select_bedfiles_for_bedset(condition=\"name=%s\", condition_val=[\"bedsetOver1kRegions\"], bedfile_col=[\"id\", \"name\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The unwanted relationships can be removed with `BedBaseConf.remove_relationship` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "bbc.remove_relationship(bedfile_ids=[3], bedset_id=2)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs_jupyter/build/.gitignore b/docs_jupyter/build/.gitignore deleted file mode 100644 index d6b7ef3..0000000 --- a/docs_jupyter/build/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -* -!.gitignore diff --git a/mkdocs.yml b/mkdocs.yml index e635b92..6194fdb 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -25,7 +25,7 @@ markdown_extensions: custom_fences: - name: mermaid class: mermaid - format: !!python/name:pymdownx.superfences.fence_code_format + format: "!!python/name:pymdownx.superfences.fence_code_format" extra_css: - stylesheets/extra.css @@ -39,6 +39,7 @@ copyright: > nav: + - Home: README.md - BEDbase: - BEDbase: bedbase/README.md - UI guide: @@ -49,15 +50,24 @@ nav: - Changelog: bedhost/changelog.md - BBConf: - BBConf: bbconf/README.md + - Configuring: bbconf/code/config.md + - Demo: bbconf/code/demo.md - Guide: bbconf/bbc_api.md - Changelog: bbconf/changelog.md - Reference: + - How to cite: citations.md - Usage: bedboss_usage.md - Support: https://github.com/bedbase/bedboss/issues - Contributing: contributing.md - Changelog: changelog.md - BEDboss: - BEDBoss: bedboss/README.md + - Tutorials: + - BEDbase tutorial: bedboss/code/bedbase-tutorial.md + - BEDmaker tutorial: bedboss/code/bedmaker-tutorial.md + - BEDqc tutorial: bedboss/code/bedqc-tutorial.md + - BEDstat tutorial: bedboss/code/bedstat-tutorial.md + - Everything tutorial: bedboss/code/tutorial-all.md - How to guides: - Configure bedboss: bedboss/how-to-configure.md - Run from Python: bedboss/how-to-run-from-python.md @@ -65,26 +75,28 @@ nav: - Create BEDbase database: bedboss/how-to-create-database.md - BEDboss insert: bedboss/bedboss-insert.md - Reference: + - How to cite: citations.md - Usage: bedboss/usage.md - Geniml: - Geniml: geniml/README.md - Getting Started: - Module overviews: geniml/modules.md - How-to guides: - - Assess universe fit: geniml/tutorials/assess-universe.md - - Search intervals with BEDSpace: geniml/tutorials/bedspace.md - - Evaluate embeddings: geniml/tutorials/evaluation.md - Train region2vec embeddings: geniml/tutorials/region2vec.md - Train single-cell embeddings: geniml/tutorials/train-scembed-model.md - - Load vector database with embeddings: tutorials/load-qdrant-with-cell-embeddings.md - - Cell-type prediction using KNN: tutorials/cell-type-annotation-with-knn.md - Tokenization: geniml/tutorials/tokenization.md - Tokenize a BED file on the command line: geniml/tutorials/cli-tokenization.md + - Evaluate embeddings: geniml/tutorials/evaluation.md + - Search intervals with BEDSpace: geniml/tutorials/bedspace.md + - Load vector database with embeddings: tutorials/load-qdrant-with-cell-embeddings.md + - Cell-type prediction using KNN: tutorials/cell-type-annotation-with-knn.md - Create consensus peaks: geniml/tutorials/create-consensus-peaks.md + - Assess universe fit: geniml/tutorials/assess-universe.md - Fine-tune embeddings: geniml/tutorials/fine-tune-region2vec-model.md - Randomize bed files: geniml/tutorials/bedshift.md - Create evaluation dataset with bedshift: geniml/tutorials/bedshift-evaluation-guide.md - Reference: + - How to cite: citations.md - API: geniml/autodoc_build/geniml.md - Support: geniml/support.md - Contributing: geniml/contributing.md @@ -93,16 +105,7 @@ nav: - Genimtools: genimtools/README.md - How to cite: citations.md -jupyter: - - in: eido/notebooks - out: eido/code - - in: geofetch/notebooks - out: geofetch/code - - in: looper/notebooks - out: looper/code - - in: peppy/notebooks - out: peppy/code - - in: pipestat/notebooks - out: pipestat/code - - in: pypiper/notebooks - out: pypiper/code +autodoc: + jupyter: + - in: bbconf/notebooks + out: bbconf/code From 2a2d736eb97fbcadac55c27ef5a09096d5898a75 Mon Sep 17 00:00:00 2001 From: nsheff Date: Mon, 19 Feb 2024 21:23:52 -0500 Subject: [PATCH 06/17] add bedspace analysis --- autodoc.py | 2 +- docs/bbconf/code/config.md | 15333 ++++++++++++++- docs/bbconf/code/demo.md | 16050 +++++++++++++++- docs/geniml/code/bedspace-analysis.md | 15574 +++++++++++++++ docs/geniml/notebooks/bedspace-analysis.ipynb | 314 + docs/geniml/notebooks/distance_l2r.csv.gz | Bin 0 -> 1118727 bytes docs/geniml/notebooks/distance_r2r.csv.gz | Bin 0 -> 99399 bytes mkdocs.yml | 8 +- 8 files changed, 47061 insertions(+), 220 deletions(-) create mode 100644 docs/geniml/code/bedspace-analysis.md create mode 100755 docs/geniml/notebooks/bedspace-analysis.ipynb create mode 100755 docs/geniml/notebooks/distance_l2r.csv.gz create mode 100755 docs/geniml/notebooks/distance_r2r.csv.gz diff --git a/autodoc.py b/autodoc.py index e5c7aa1..94d36f9 100644 --- a/autodoc.py +++ b/autodoc.py @@ -94,7 +94,7 @@ bn, _ = os.path.splitext(os.path.basename(nb)) out = f"docs/{item['out']}/{bn}.md" print(f"Converting '{nb}' to '{out}'") - md_result = nbconvert.exporters.export(nbconvert.MarkdownExporter(), nb)[0] + md_result = nbconvert.exporters.export(nbconvert.HTMLExporter(), nb)[0] Path(os.path.dirname(out)).mkdir(parents=True, exist_ok=True) with open(out, "w") as stream: stream.write(md_result) diff --git a/docs/bbconf/code/config.md b/docs/bbconf/code/config.md index ea8e269..fd25f1f 100644 --- a/docs/bbconf/code/config.md +++ b/docs/bbconf/code/config.md @@ -1,80 +1,15301 @@ -# Bedbase configuration file documentation + + + + -In order to start working with the `BedBaseConf` object, it has to be initialized first. The constuctor requires one argument, which is a path to the configuration file (in YAML format). +config -## Minimal config file -The minimal configuration must define the `path` section with 3 keys: -- `pipeline_output_path`: path to the desired output directory for the pipelines -- `bedstat_dir`: name of the [bedstat](https://github.com/databio/bedstat) pipeline output directory -- `bedbuncher_dir`: name of the [bedbuncher](https://github.com/databio/bedbuncher) pipeline output directory -Here's an example of a minimal bedbase configuration file: + -```python -!cat ../tests/data/config_min.yaml -``` - # min config example. Refer to bbconf/const.py for key names and default values - - path: - pipeline_output_path: $HOME/bedbase - bedstat_dir: bedstat_output - bedbuncher_dir: bedbuncher_output + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/bbconf/code/demo.md b/docs/bbconf/code/demo.md index c4dcfff..1b363e2 100644 --- a/docs/bbconf/code/demo.md +++ b/docs/bbconf/code/demo.md @@ -1,302 +1,16030 @@ -# `BedBaseConf` object usage demonstration + + + + + +demo + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - [[1, 'GSE105587_ENCFF018NNF_conservative_idr_thresholded_peaks_GRCh38'], - [2, 'GSE105977_ENCFF617QGK_optimal_idr_thresholded_peaks_GRCh38'], - [3, 'GSE105977_ENCFF793SZW_conservative_idr_thresholded_peaks_GRCh38'], - [4, 'GSE105977_ENCFF937CGY_peaks_GRCh38'], - [5, 'GSE91663_ENCFF316ASR_peaks_GRCh38'], - [6, 'GSE91663_ENCFF319TPR_conservative_idr_thresholded_peaks_GRCh38'], - [7, 'GSE91663_ENCFF553KIK_optimal_idr_thresholded_peaks_GRCh38'], - [8, 'GSM2423312_ENCFF155HVK_peaks_GRCh38'], - [9, 'GSM2423313_ENCFF722AOG_peaks_GRCh38'], - [10, 'GSM2827349_ENCFF196DNQ_peaks_GRCh38'], - [11, 'GSM2827350_ENCFF928JXU_peaks_GRCh38']] -The unwanted relationships can be removed with `BedBaseConf.remove_relationship` method: -```python -bbc.remove_relationship(bedfile_ids=[3], bedset_id=2) -``` + diff --git a/docs/geniml/code/bedspace-analysis.md b/docs/geniml/code/bedspace-analysis.md new file mode 100644 index 0000000..7e94fa9 --- /dev/null +++ b/docs/geniml/code/bedspace-analysis.md @@ -0,0 +1,15574 @@ + + + + + +bedspace-analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/geniml/notebooks/bedspace-analysis.ipynb b/docs/geniml/notebooks/bedspace-analysis.ipynb new file mode 100755 index 0000000..edacb79 --- /dev/null +++ b/docs/geniml/notebooks/bedspace-analysis.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import glob\n", + "import numpy as np\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams[\"svg.fonttype\"] = \"none\"\n", + "matplotlib.rcParams[\"text.usetex\"] = False\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scenario 1: return a list most similar region sets and thier similarity score to a query metadata label (l2r)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Path to the pre-calcualted distance file between label embedding and region set embeddings\n", + "path_simfile = './distance_l2r.csv'\n", + "distance = pd.read_csv(path_simfile)\n", + "distance.file_label = distance.file_label.str.lower()\n", + "distance.search_term = distance.search_term.str.lower()\n", + "distance = distance.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['h3k4me3' 'h3k27me3' 'h3k27ac' 'h3k4me1' 'h3k9me3' 'h3k4me2' 'h3k9ac'\n", + " 'h3k79me2' 'h4k20me1' 'h3k9me2' 'h3k9me1']\n" + ] + } + ], + "source": [ + "# Print the search terms (labels)\n", + "print(distance.search_term.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def S1(searchterm, distance):\n", + " nof = len(distance[distance.file_label.str.contains(searchterm)])\n", + " df = distance[distance.search_term == searchterm].sort_values(by=['score'], ascending = False)[0:10]\n", + " df = df.sort_values(by=['score'], ascending=True)\n", + " df['color']='gray'\n", + " df.loc[df.file_label.str.contains(searchterm), 'color'] = 'green'\n", + " if(len(df[df.color == 'green']) == nof):\n", + " df.loc[(df.color!='green'), 'color'] = 'gray'\n", + " \n", + " plt= df.plot.barh(x='filename', y='score', figsize=(10,7), fontsize=16, color=list(df['color']))\n", + " plt.set_xlabel('Search term:' + searchterm, fontsize=15)\n", + "\n", + " plt.axis(xmin=0.5, xmax=1.01)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "S1('h3k4me2', distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scenario 2: return a list of most similar labels and thier similarity score to a query region set (r2l)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['gse124683/gsm3540312_k562_h3k4me2_rep1_20181202_icell8_21.bed.gz', 'gse175750/gsm5345532_kdm4a-oe.h3k9ac.ls.rep2.bed.gz', 'gse161624/gsm4911338_etv6_ncoa2_tm_hcd34_h3k27ac_vs_total_peaks.narrowpeak.gz', 'gse124690/gsm3540920_k562_h3k4me2_rep2_20181202_icell8_215.bed.gz']\n" + ] + } + ], + "source": [ + "# Print a sample of filenames\n", + "\n", + "print(list(set(distance.filename))[0:4])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def S2(file, distance):\n", + " df = distance[distance.filename == file].sort_values(by=['score'], ascending = False)[0:10]\n", + " df= df.sort_values(by=['score'], ascending = True)\n", + " df['color']='green'\n", + " plt= df.plot.barh(x='search_term', y='score', figsize=(8,5), fontsize=16, color=list(df['color']))\n", + " plt.set_xticks(np.arange(0.5,1.1, 0.1))\n", + " plt.set_ylabel('Similarity', fontsize=15)\n", + " plt.set_xlabel(file, fontsize=15)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "S2('gse156613/gsm4743940_t52-h3k27ac_peaks.bed.gz', distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scenario 3: return a list of most similar region sets and thier similarity scores to a query region set (r2r)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "file_name = './distance_r2r.csv'\n", + "distance_s3 = pd.read_csv(file_name)\n", + "distance_s3.score = 1 - distance_s3.score" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['ENCFF538OAF.bed.gz,h3k4me1',\n", + " 'ENCFF494ASP.bed.gz,h3k27me3',\n", + " 'ENCFF292YSJ.bed.gz,h3k4me3',\n", + " 'ENCFF001WUM.bed.gz,h3k4me3',\n", + " 'ENCFF701ENX.bed.gz,h3k27ac',\n", + " 'ENCFF727GSV.bed.gz,h3k27ac',\n", + " 'ENCFF787BOJ.bed.gz,h3k27me3',\n", + " 'ENCFF526BJR.bed.gz,h3k27ac',\n", + " 'ENCFF908TKC.bed.gz,h3k9me3',\n", + " 'ENCFF099WAJ.bed.gz,h4k20me1']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print sample query region set\n", + "list(set(distance_s3.test_file))[0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def S3(query_file, distance_s3):\n", + " \n", + " df = distance_s3[distance_s3.test_file==query_file].sort_values(by ='score', ascending = False)[['test_file', 'train_file', 'score']]\n", + " df['label_test'] = df.test_file.str.split(',', expand = True)[1]\n", + " df['label_train'] = df.train_file.str.split(',', expand = True)[1]\n", + " \n", + " nof = len(df[df.label_test==df.label_train])\n", + " df = df[0:10]\n", + " \n", + "\n", + " df=df.sort_values(by=['score'])\n", + " df['color']='gray'\n", + " df.loc[df.label_test==df.label_train, 'color'] = 'green'\n", + " \n", + " if(len(df[df.color=='green']) ==nof):\n", + "\n", + " df.loc[(df.color!='green'), 'color'] = 'gray'\n", + " \n", + " plt= df.plot.barh(x='train_file', y='score', figsize=(10,7), fontsize=16, color=list(df['color']))\n", + " plt.axis(xmin=0.7,xmax=1.01)\n", + " \n", + " plt.set_ylabel('Similarity', fontsize=15)\n", + " plt.set_xlabel(query_file, fontsize=15)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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z@7sAfx9=|lb*BBjsMo7ur-*zaQ){rY8FG7^E%R(Oj|W`r14RdIJ0EeQ!Re_9O-Zto z6JVD8!tU6zd{K&O25*V&PvS%IrwMK<68HOB&H4KNpv~=Va+}?-xR+QLP>E=6iMTdM z1<{Q%rP%!$oR_~ZdS+}d2?1sTnf;Uz5!o2)w9F>BL(z)6e!J9Oy)Nv_%9jnjjZMiV zBa-3gjeqsqYrP)RR5BRS2S&K4?SpvmjN3a;4otKuD!Du#;q>&ww zdbFSX9Dgn~1b=I8MwAuyKh$@Q=^g5nl1)3IE);X~pao|&ib(Y!aWezHqqVUUJ=tMe zR@ceS|2&^s*hn0KK!q49doUvbzd612QZ(kxt0qn<59#&id-UQN-64%*BW`G~}kEnOXc-&wxn2bw8mJ3m)H9r6Hcm;Qi5sk&&o$ zatkU@mg+2-;PU1{6I9gYb_&c~Sg-JY@wAi!(1)0kt$)7N5s?JuubWX6MN!UDID2Gg zV1$5dUNAx~-cI_LzbE7K#mng^vZ<~_T!}F50HMI>A@=558p5ADH7n_}hk%}%fy5Em z12q>dfExQN=;Ph7sg+xN>X<>DJZTxAWRRB0L9r=4f7a^XkChMq1I5P5ry-3lKU&Hs PDR2KjjY>zaOC<#W?Rj@J literal 0 HcmV?d00001 diff --git a/mkdocs.yml b/mkdocs.yml index 6194fdb..fbb4fcc 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -95,6 +95,8 @@ nav: - Fine-tune embeddings: geniml/tutorials/fine-tune-region2vec-model.md - Randomize bed files: geniml/tutorials/bedshift.md - Create evaluation dataset with bedshift: geniml/tutorials/bedshift-evaluation-guide.md + - Tutorials: + - Using BEDspace: geniml/code/bedspace-analysis.md - Reference: - How to cite: citations.md - API: geniml/autodoc_build/geniml.md @@ -107,5 +109,7 @@ nav: autodoc: jupyter: - - in: bbconf/notebooks - out: bbconf/code + - in: bbconf/notebooks + out: bbconf/code + - in: geniml/notebooks + out: geniml/code \ No newline at end of file From c1627417adc880e612a4347c24fcabc0cb2d1a4e Mon Sep 17 00:00:00 2001 From: nsheff Date: Tue, 20 Feb 2024 08:09:24 -0500 Subject: [PATCH 07/17] update docs --- docs/geniml/changelog.md | 4 ++++ mkdocs.yml | 20 +++++++++++--------- 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/docs/geniml/changelog.md b/docs/geniml/changelog.md index 6acc545..ae8188c 100644 --- a/docs/geniml/changelog.md +++ b/docs/geniml/changelog.md @@ -2,6 +2,10 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html) and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format. +## [0.2.0] -- 2024-02-20 + +- Fixed a bug with pydantic + ## [0.1.0] -- 2023-12-18 - First official released version of geniml diff --git a/mkdocs.yml b/mkdocs.yml index fbb4fcc..2a0cdae 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -82,24 +82,26 @@ nav: - Getting Started: - Module overviews: geniml/modules.md - How-to guides: - - Train region2vec embeddings: geniml/tutorials/region2vec.md - - Train single-cell embeddings: geniml/tutorials/train-scembed-model.md + - Cache BED files with BBClient: geniml/tutorials/bbclient.md - Tokenization: geniml/tutorials/tokenization.md - - Tokenize a BED file on the command line: geniml/tutorials/cli-tokenization.md + - Tokenize a BED file with CLI: geniml/tutorials/cli-tokenization.md + - Train region2vec embeddings: geniml/tutorials/region2vec.md + - Train BEDspace model: + - Train BEDSpace embeddings: geniml/tutorials/bedspace.md + - BEDspace results notebook: geniml/code/bedspace-analysis.md + - Use scEmbed model: + - Train single-cell embeddings: geniml/tutorials/train-scembed-model.md + - Populate a vector store: geniml/tutorials/load-qdrant-with-cell-embeddings.md + - Predict cell-types using KNN: geniml/tutorials/cell-type-annotation-with-knn.md - Evaluate embeddings: geniml/tutorials/evaluation.md - - Search intervals with BEDSpace: geniml/tutorials/bedspace.md - - Load vector database with embeddings: tutorials/load-qdrant-with-cell-embeddings.md - - Cell-type prediction using KNN: tutorials/cell-type-annotation-with-knn.md - Create consensus peaks: geniml/tutorials/create-consensus-peaks.md - Assess universe fit: geniml/tutorials/assess-universe.md - Fine-tune embeddings: geniml/tutorials/fine-tune-region2vec-model.md - Randomize bed files: geniml/tutorials/bedshift.md - Create evaluation dataset with bedshift: geniml/tutorials/bedshift-evaluation-guide.md - - Tutorials: - - Using BEDspace: geniml/code/bedspace-analysis.md - Reference: - How to cite: citations.md - - API: geniml/autodoc_build/geniml.md + - API documentation: geniml/autodoc_build/geniml.md - Support: geniml/support.md - Contributing: geniml/contributing.md - Changelog: geniml/changelog.md From d63a083d9f3bbc0852062d95b6b766305465276b Mon Sep 17 00:00:00 2001 From: Nathan LeRoy <41063083+nleroy917@users.noreply.github.com> Date: Tue, 20 Feb 2024 09:11:09 -0500 Subject: [PATCH 08/17] Update changelog.md --- docs/geniml/changelog.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/geniml/changelog.md b/docs/geniml/changelog.md index ae8188c..a3ec7c7 100644 --- a/docs/geniml/changelog.md +++ b/docs/geniml/changelog.md @@ -5,6 +5,10 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm ## [0.2.0] -- 2024-02-20 - Fixed a bug with pydantic +- Integrate `lightning` for easier training of models with SLURM and the [DDP framework]([url](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)) +- New datasets for streaming `.gtok` files to models +- New tutorials for updated ScEmbed +- Start working on `Atacformer` ## [0.1.0] -- 2023-12-18 From 2c36aab3124cb3e74f51885dc3d6345047245407 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy <41063083+nleroy917@users.noreply.github.com> Date: Tue, 20 Feb 2024 09:11:38 -0500 Subject: [PATCH 09/17] fix link --- docs/geniml/changelog.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/geniml/changelog.md b/docs/geniml/changelog.md index a3ec7c7..f932c33 100644 --- a/docs/geniml/changelog.md +++ b/docs/geniml/changelog.md @@ -5,7 +5,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm ## [0.2.0] -- 2024-02-20 - Fixed a bug with pydantic -- Integrate `lightning` for easier training of models with SLURM and the [DDP framework]([url](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)) +- Integrate `lightning` for easier training of models with SLURM and the [DDP framework](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) - New datasets for streaming `.gtok` files to models - New tutorials for updated ScEmbed - Start working on `Atacformer` From 1fac94906faaed45dae26b79ff06047b1a5f2e6f Mon Sep 17 00:00:00 2001 From: Khoroshevskyi Date: Mon, 26 Feb 2024 22:32:25 +0100 Subject: [PATCH 10/17] updated bedboss docs 1 --- docs/bedboss/changelog.md | 7 ----- docs/bedboss/how-to-install-r-dependencies.md | 15 ++++------ docs/bedboss/installRdeps.R | 29 ------------------- docs/bedboss/run_bedboss_all_tutorial.md | 1 + docs/bedboss/templates/usage.template | 22 -------------- 5 files changed, 6 insertions(+), 68 deletions(-) delete mode 100644 docs/bedboss/changelog.md delete mode 100644 docs/bedboss/installRdeps.R create mode 100644 docs/bedboss/run_bedboss_all_tutorial.md delete mode 100644 docs/bedboss/templates/usage.template diff --git a/docs/bedboss/changelog.md b/docs/bedboss/changelog.md deleted file mode 100644 index 5026ad7..0000000 --- a/docs/bedboss/changelog.md +++ /dev/null @@ -1,7 +0,0 @@ -# Changelog - -This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html) and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format. - -## [0.1.0a1] - 2023-08-02 -### Added -- Initial alpha release diff --git a/docs/bedboss/how-to-install-r-dependencies.md b/docs/bedboss/how-to-install-r-dependencies.md index 9619097..ec54fc6 100644 --- a/docs/bedboss/how-to-install-r-dependencies.md +++ b/docs/bedboss/how-to-install-r-dependencies.md @@ -1,12 +1,7 @@ # How to install R dependencies -1. Install R: -2. Download this script: Install R dependencies -3. Install dependencies by running this command in your terminal: - - ``` - Rscript installRdeps.R - ``` - -4. Run `bash_requirements_test.sh` to check if everything was installed correctly (located in test folder: -[Bash requirement tests](https://github.com/bedbase/bedboss/blob/68910f5142a95d92c27ef53eafb9c35599af2fbd/test/bash_requirements_test.sh)) +0. Install bedboss +1. Install R: https://cran.r-project.org/bin/linux/ubuntu/fullREADME.html +2. Download this script: [installRdeps.R](https://github.com/databio/bedboss/blob/dev/scripts/installRdeps.R) +3. Install dependencies by running this command in your terminal: ```Rscript installRdeps.R``` +4. Run `bedboss requirements-check` to check if everything was installed correctly. diff --git a/docs/bedboss/installRdeps.R b/docs/bedboss/installRdeps.R deleted file mode 100644 index 6e6627e..0000000 --- a/docs/bedboss/installRdeps.R +++ /dev/null @@ -1,29 +0,0 @@ -.install_pkg = function(p, bioc=FALSE) { - if(!require(package = p, character.only=TRUE)) { - if(bioc) { - BiocManager::install(pkgs = p) - } else { - install.packages(pkgs = p) - } - } -} - -.install_pkg("R.utils") -.install_pkg("BiocManager") -.install_pkg("optparse") -.install_pkg("devtools") -.install_pkg("GenomicRanges", bioc=TRUE) -.install_pkg("GenomicFeatures", bioc=TRUE) -.install_pkg("ensembldb", bioc=TRUE) -.install_pkg("LOLA", bioc=TRUE) -.install_pkg("BSgenome", bioc=TRUE) -.install_pkg("ExperimentHub", bioc=TRUE) -.install_pkg("AnnotationHub", bioc=TRUE) -.install_pkg("conflicted") -if(!require(package = "GenomicDistributions", character.only=TRUE)) { - devtools::install_github("databio/GenomicDistributions") -} -options(timeout=1000) -if(!require(package = "GenomicDistributionsData", character.only=TRUE)) { - install.packages("http://big.databio.org/GenomicDistributionsData/GenomicDistributionsData_0.0.2.tar.gz", repos=NULL) -} diff --git a/docs/bedboss/run_bedboss_all_tutorial.md b/docs/bedboss/run_bedboss_all_tutorial.md new file mode 100644 index 0000000..518e677 --- /dev/null +++ b/docs/bedboss/run_bedboss_all_tutorial.md @@ -0,0 +1 @@ +# Bedboss basic tutorial diff --git a/docs/bedboss/templates/usage.template b/docs/bedboss/templates/usage.template deleted file mode 100644 index d01300f..0000000 --- a/docs/bedboss/templates/usage.template +++ /dev/null @@ -1,22 +0,0 @@ -# Usage reference - -BEDboss is command-line tool-warehouse of 3 pipelines for genomic interval files - -BEDboss include: bedmaker, bedqc, bedstat. This pipelines can be run using next positional arguments: - -- `bedbase all`: Runs all pipelines one in order: bedmaker -> bedqc -> bedstat - -- `bedbase insert`: Runs all pipelines one in order by using PEP file and creates bedset: bedmaker -> bedqc -> bedstat -> bedbuncher - -- `bedbase make`: Creates Bed and BigBed files from other type of genomic interval files [bigwig|bedgraph|bed|bigbed|wig] - -- `bedbase qc`: Runs Quality control for bed file (Works only with bed files) - -- `bedbase stat`: Runs statistics for bed and bigbed files. - -- `bedbase bunch`: Creates bedset from PEP file - -- `bedbase index`: Creates bed file vectors and inserts to qdrant database - -Here you can see the command-line usage instructions for the main bedboss command and for each subcommand: - From e557798f3edc8be1cf0c179c16285732b7362cfb Mon Sep 17 00:00:00 2001 From: Khoroshevskyi Date: Mon, 26 Feb 2024 22:42:36 +0100 Subject: [PATCH 11/17] updated bedboss docs 2 --- docs/bedboss/README.md | 32 ++++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/docs/bedboss/README.md b/docs/bedboss/README.md index ed9a7ea..20ff5d4 100644 --- a/docs/bedboss/README.md +++ b/docs/bedboss/README.md @@ -1,5 +1,33 @@ -# BEDboss -bedboss is a command-line pipeline that standardizes and calculates statistics for genomic interval data, and enters the results into a BEDbase database. +# bedboss + +[![PEP compatible](https://pepkit.github.io/img/PEP-compatible-green.svg)](https://pep.databio.org/) +![Run pytests](https://github.com/bedbase/bedboss/workflows/Run%20instalation%20test/badge.svg) +[![pypi-badge](https://img.shields.io/pypi/v/bedboss?color=%2334D058)](https://pypi.org/project/bedboss) +[![pypi-version](https://img.shields.io/pypi/pyversions/bedboss.svg?color=%2334D058)](https://pypi.org/project/bedboss) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![Github badge](https://img.shields.io/badge/source-github-354a75?logo=github)](https://github.com/databio/bedboss) + +--- + +**Documentation**: https://docs.bedbase.org/bedboss + +**Source Code**: https://github.com/databio/bedboss + +--- + +bedboss is a command-line pipeline that filters, standardizes, and calculates statistics for genomic interval data, +and enters the results into a BEDbase database. + +## Installation +To install `bedboss` use this command: +``` +pip install bedboss +``` +or install the latest version from the GitHub repository: +``` +pip install git+https://github.com/databio/bedboss.git +``` + It has 3 components: 1) bedmaker (`bedboss make`);
From 3a3aef300f5baa31b4b56265a34c449b8ae888e0 Mon Sep 17 00:00:00 2001 From: Khoroshevskyi Date: Mon, 26 Feb 2024 22:54:40 +0100 Subject: [PATCH 12/17] updated bedboss docs 3 --- docs/bedboss/README.md | 55 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/docs/bedboss/README.md b/docs/bedboss/README.md index 20ff5d4..5eac8ad 100644 --- a/docs/bedboss/README.md +++ b/docs/bedboss/README.md @@ -91,3 +91,58 @@ regionstat.R script is used to calculate the bed file statistics, so the pipelin All dependencies you can find in R helper script, and use it to easily install the required packages: - Rscript scripts/installRdeps.R [How to install R dependencies](./how_to_install_r_dep.md) + + + +--- + +# OTHER docs: + + + + + +It has 3 components: 1) bedmaker (`bedboss make`); 2) bedqc (`bedboss qc`); and 3) bedstat `bedboss stat`. You may run all 3 pipelines separately, together (`bedbase all`). +## 1) bedmaker + +Converts supported file types into BED and bigBed format. Currently supported formats: + - bedGraph + - bigBed + - bigWig + - wig + +## 2) bedqc + +Assess QC of BED files and flag potential problems for further evaluation so you can determine whether they should be included in downstream analysis. +Currently, it flags BED files that are larger than 2 GB, have over 5 million regions, or have a mean region width less than 10 bp. +These thresholds can be changed with pipeline arguments. + +## 3) bedstat + +Calculates statistics about BED files. + +## 4) bedbuncher + +Creates **bedsets** (sets of BED files) and calculates statistics about them (currently means and standard deviations). + +## Additional bedboss components: +### Indexing +bedboss can automatically create vector embeddings for BED files using geniml. And later these embeddings can +be automatically inserted into the qdrant database. + +### Uploading to s3 +bedboss can automatically upload files to an s3 bucket. This can be done using `--upload-to-s3` flag. + +--- + +# Documentation +Full documentation is available at [bedboss.databio.org](https://docs.bedbase.org/). + +## How to install R dependencies + +1. Install R: https://cran.r-project.org/bin/linux/ubuntu/fullREADME.html +2. Install dev tools on linux: ```sudo apt install r-cran-devtools``` +3. Download script `installRdeps.R` from this repository. +4. Install dependencies by running this command in your terminal: ```Rscript installRdeps.R``` +5. Run `bash_requirements_test.sh` to check if everything was installed correctly (located in test folder: +[Bash requirement tests](https://github.com/bedbase/bedboss/blob/68910f5142a95d92c27ef53eafb9c35599af2fbd/test/bash_requirements_test.sh) From 5bcadd850ac82c46279d2916a309aded0d2b34b8 Mon Sep 17 00:00:00 2001 From: Khoroshevskyi Date: Tue, 27 Feb 2024 18:17:41 +0100 Subject: [PATCH 13/17] updated bedboss docs 4 --- docs/bedboss/how-to-configure.md | 42 ++++------ docs/bedboss/how-to-create-database.md | 15 +++- docs/bedboss/how-to-run-from-python.md | 77 ------------------- docs/bedboss/run_bedboss_all_tutorial.md | 1 - docs/bedboss/tutorials/bedbuncher_tutorial.md | 1 + .../tutorials/bedclassifier_tutorial.md | 1 + docs/bedboss/tutorials/bedindex_tutorial.md | 1 + docs/bedboss/tutorials/bedmaker_tutorial.md | 1 + docs/bedboss/tutorials/bedqc_tutorial.md | 1 + docs/bedboss/tutorials/bedstat_tutorial.md | 1 + docs/bedboss/tutorials/tutorial_all.md | 1 + docs/bedboss/tutorials/tutorial_insert.md | 1 + mkdocs.yml | 21 ++--- 13 files changed, 44 insertions(+), 120 deletions(-) delete mode 100644 docs/bedboss/how-to-run-from-python.md delete mode 100644 docs/bedboss/run_bedboss_all_tutorial.md create mode 100644 docs/bedboss/tutorials/bedbuncher_tutorial.md create mode 100644 docs/bedboss/tutorials/bedclassifier_tutorial.md create mode 100644 docs/bedboss/tutorials/bedindex_tutorial.md create mode 100644 docs/bedboss/tutorials/bedmaker_tutorial.md create mode 100644 docs/bedboss/tutorials/bedqc_tutorial.md create mode 100644 docs/bedboss/tutorials/bedstat_tutorial.md create mode 100644 docs/bedboss/tutorials/tutorial_all.md create mode 100644 docs/bedboss/tutorials/tutorial_insert.md diff --git a/docs/bedboss/how-to-configure.md b/docs/bedboss/how-to-configure.md index d41da3a..27dda82 100644 --- a/docs/bedboss/how-to-configure.md +++ b/docs/bedboss/how-to-configure.md @@ -1,11 +1,11 @@ -# How to create bedbase config file (for bedstat) +# How to create bedbase config file ### Bedbase config file is yaml file with 4 parts: -- path to output files -- database credentials -- qdrant credentials (can be skipped for if indexing is not needed) -- server information -- remote info (can be skipped for bedboss) +- paths and vector models +- relational database credentials +- qdrant credentials +- server information +- remote info ### Example: ```yaml @@ -14,7 +14,7 @@ path: pipeline_output_path: /data/outputs bedstat_dir: outputs/bedstat_output bedbuncher_dir: outputs/bedbuncher_output - region2vec: databio/r2v-ChIP-atlas-hg38 + region2vec: databio/r2v-ChIP-atlas-hg38-v2 vec2vec: databio/v2v-MiniLM-v2-ATAC-hg38 text2vec: sentence-transformers/all-MiniLM-L6-v2 database: @@ -23,8 +23,11 @@ database: password: $POSTGRES_PASSWORD user: $POSTGRES_USER name: bedbase + bed_table: bedfiles + bedset_table: bedsets + relationship_table: bedset_bedfiles dialect: postgresql - driver: psycopg + driver: psycopg2 qdrant: host: $QDRANT_HOST port: 6333 @@ -35,26 +38,9 @@ server: port: 8000 remotes: http: - prefix: http://data.bedbase.org/ - description: HTTP compatible path - s3: - prefix: s3://data.bedbase.org/ - description: S3 compatible path -access_methods: - http: - type: "https" - description: HTTP compatible path prefix: https://data2.bedbase.org/ + description: HTTP compatible path s3: - type: "s3" - description: S3 compatible path prefix: s3://data2.bedbase.org/ - local: - type: "https" - description: How to serve local files. - prefix: /static/ -``` - -Download example bedbase configuration file here: Example bedbase configuration file - -. \ No newline at end of file + description: S3 compatible path +``` \ No newline at end of file diff --git a/docs/bedboss/how-to-create-database.md b/docs/bedboss/how-to-create-database.md index 08ee2f2..7dbac03 100644 --- a/docs/bedboss/how-to-create-database.md +++ b/docs/bedboss/how-to-create-database.md @@ -1,9 +1,14 @@ -# How to create bedbase database +# How to create BEDbase database -To run bedstat, bedbuncher and bedmbed we need to create postgres database. +To run bedboss and upload data to the database we need to create postgres database, or use existing one. +--- +### To create local database: We are initiating postgres db in docker. -If you don't have docker installed, you can install it with `sudo apt-get update && apt-get install docker-engine -y`. +If you don't have docker installed, you can install it with +```bash +sudo apt-get update && apt-get install docker-engine -y +``` Now, create a persistent volume to house PostgreSQL data: @@ -16,7 +21,9 @@ docker run -d --name bedbase-postgres -p 5432:5432 \ -e POSTGRES_PASSWORD=bedbasepassword \ -e POSTGRES_USER=postgres \ -e POSTGRES_DB=postgres \ - -v postgres-data:/var/lib/postgresql/data postgres:13 + -v postgres-data:/var/lib/postgresql/data \ + postgres:13 ``` Now we have created docker and can run pipelines. +To connect to the database, change your credentials in the `bedbase_config.yaml` file. diff --git a/docs/bedboss/how-to-run-from-python.md b/docs/bedboss/how-to-run-from-python.md deleted file mode 100644 index c45c814..0000000 --- a/docs/bedboss/how-to-run-from-python.md +++ /dev/null @@ -1,77 +0,0 @@ -# How to run bedboss as a Python API - -## Install bedboss - -```bash -pip install bedboss -``` - -## Run bedboss all - -```python -from bedboss import run_all - -run_all( - sample_name="example_sample_name", - input_file="example/path/to/input_file", - input_type="bed", - outfolder="example/path/to/outfolder", - genome="hg38", - bedbase_config="example/path/to/bedbase_config.yaml", - # + another optional arguments -) - - -``` - - -## Run bedboss all-pep - -```python -from bedboss import run_all_by_pep - -run_all_by_pep( - pep="example/path/to/pep.yaml" -) -``` - -## Run bedboss make - -```python -from bedboss import BedMaker - -BedMaker( - input_file="example/path/to/input_file", - input_type="bed", - output_bed="example/path/to/output_bed", - output_bigbed="example/path/to/output_bigbed", - sample_name="example_sample_name", - genome="hg38", -) - -``` - -## Run bedboss stat - -```python -from bedboss import bedstat - -bedstat( - bedfile="example/path/to/bedfile.bed", - bedbase_config="example/path/to/bedbase_config.yaml", - genome="hg38", - outfolder="example/path/to/outfolder", -) - -``` - -## Run bedboss qc - -```python -from bedboss import bedqc - -bedqc( - bedfile="example/path/to/bedfile.bed", - outfolder="example/path/to/outfolder", -) -``` \ No newline at end of file diff --git a/docs/bedboss/run_bedboss_all_tutorial.md b/docs/bedboss/run_bedboss_all_tutorial.md deleted file mode 100644 index 518e677..0000000 --- a/docs/bedboss/run_bedboss_all_tutorial.md +++ /dev/null @@ -1 +0,0 @@ -# Bedboss basic tutorial diff --git a/docs/bedboss/tutorials/bedbuncher_tutorial.md b/docs/bedboss/tutorials/bedbuncher_tutorial.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/bedbuncher_tutorial.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/docs/bedboss/tutorials/bedclassifier_tutorial.md b/docs/bedboss/tutorials/bedclassifier_tutorial.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/bedclassifier_tutorial.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/docs/bedboss/tutorials/bedindex_tutorial.md b/docs/bedboss/tutorials/bedindex_tutorial.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/bedindex_tutorial.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/docs/bedboss/tutorials/bedmaker_tutorial.md b/docs/bedboss/tutorials/bedmaker_tutorial.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/bedmaker_tutorial.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/docs/bedboss/tutorials/bedqc_tutorial.md b/docs/bedboss/tutorials/bedqc_tutorial.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/bedqc_tutorial.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/docs/bedboss/tutorials/bedstat_tutorial.md b/docs/bedboss/tutorials/bedstat_tutorial.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/bedstat_tutorial.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/docs/bedboss/tutorials/tutorial_all.md b/docs/bedboss/tutorials/tutorial_all.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/tutorial_all.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/docs/bedboss/tutorials/tutorial_insert.md b/docs/bedboss/tutorials/tutorial_insert.md new file mode 100644 index 0000000..f32c8b8 --- /dev/null +++ b/docs/bedboss/tutorials/tutorial_insert.md @@ -0,0 +1 @@ +### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 2a0cdae..aa4dd9e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -62,18 +62,19 @@ nav: - Changelog: changelog.md - BEDboss: - BEDBoss: bedboss/README.md - - Tutorials: - - BEDbase tutorial: bedboss/code/bedbase-tutorial.md - - BEDmaker tutorial: bedboss/code/bedmaker-tutorial.md - - BEDqc tutorial: bedboss/code/bedqc-tutorial.md - - BEDstat tutorial: bedboss/code/bedstat-tutorial.md - - Everything tutorial: bedboss/code/tutorial-all.md + - Tutorial: + - BEDboss-all pipeline: bedboss/tutorials/tutorial_all.md + - BEDboss insert: bedboss/tutorials/tutorial_insert.md + - BEDmaker tutorial: bedboss/tutorials/bedmaker_tutorial.md + - BEDqc tutorial: bedboss/tutorials/bedqc_tutorial.md + - BEDstat tutorial: bedboss/tutorials/bedstat_tutorial.md + - BEDbancher tutorial: bedboss/tutorials/bedbuncher_tutorial.md + - Bedindex tutorial: bedboss/tutorials/bedindex_tutorial.md + - Bedclassifier tutorial: bedboss/tutorials/bedclassifier_tutorial.md - How to guides: - - Configure bedboss: bedboss/how-to-configure.md - - Run from Python: bedboss/how-to-run-from-python.md - - Install R dependencies: bedboss/how-to-install-r-dependencies.md - Create BEDbase database: bedboss/how-to-create-database.md - - BEDboss insert: bedboss/bedboss-insert.md + - Create config file: bedboss/how-to-configure.md + - Install R dependencies: bedboss/how-to-install-r-dependencies.md - Reference: - How to cite: citations.md - Usage: bedboss/usage.md From b8be2b3117affbd711dd153584112be6e126df26 Mon Sep 17 00:00:00 2001 From: Khoroshevskyi Date: Tue, 27 Feb 2024 18:31:32 +0100 Subject: [PATCH 14/17] updated bedboss docs 5 --- docs/bedboss/how-to-develop.md | 1 + mkdocs.yml | 1 + 2 files changed, 2 insertions(+) create mode 100644 docs/bedboss/how-to-develop.md diff --git a/docs/bedboss/how-to-develop.md b/docs/bedboss/how-to-develop.md new file mode 100644 index 0000000..37d900b --- /dev/null +++ b/docs/bedboss/how-to-develop.md @@ -0,0 +1 @@ +### 🚧 docs in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index aa4dd9e..621f1b1 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -75,6 +75,7 @@ nav: - Create BEDbase database: bedboss/how-to-create-database.md - Create config file: bedboss/how-to-configure.md - Install R dependencies: bedboss/how-to-install-r-dependencies.md + - Develop process: bedboss/how-to-develop.md - Reference: - How to cite: citations.md - Usage: bedboss/usage.md From 18f37b4c1e93551813c6cb47c8669d72a2800647 Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Tue, 27 Feb 2024 15:45:07 -0500 Subject: [PATCH 15/17] Update text2bednn-search-interface.md --- .../tutorials/text2bednn-search-interface.md | 150 +++++++----------- 1 file changed, 54 insertions(+), 96 deletions(-) diff --git a/docs/geniml/tutorials/text2bednn-search-interface.md b/docs/geniml/tutorials/text2bednn-search-interface.md index e62a2e9..5c284ee 100644 --- a/docs/geniml/tutorials/text2bednn-search-interface.md +++ b/docs/geniml/tutorials/text2bednn-search-interface.md @@ -1,107 +1,42 @@ # How to create a natural language search backend for BED files -The metadata of each BED file / region set is needed to build a natural language search backend. Embedding vectors of BED -files are created by `Region2Vec`, and embedding vectors of metadata are created by [`SentenceTransformers`](https://www.sbert.net/). `Embed2EmbedNN`, -a feedforward neural network (FNN), is trained to learn the embedding vectors of metadata from the embedding vectors of BED -files. When a natural language query string is given, it will first be encoded to a vector by `SentenceTransformers`, and that -vector will be encoded to a query vector by the FNN. `search` backend can perform k-nearest neighbors (KNN) search among the -stored embedding vectors of BED files, and the BED files whose embedding vectors are closest to that query vector are the -search results. +The metadata of each BED file is needed to build a natural language search backend. BED files embedding vectors are created by +`Region2Vec`, and metadata embedding vectors are created by [`FastEmbed`](https://github.com/qdrant/fastembed), [`SentenceTransformers`](https://www.sbert.net/), or other text embedding models. -## Upload metadata and regions from files -`RegionSetInfo` is a [`dataclass`](https://docs.python.org/3/library/dataclasses.html) that can store information about a BED file, which includes the file name, metadata, and the -embedding vectors of region set and metadata. A list of RegionSetInfo can be created with a folder of BED files and a file of their -metadata by `SentenceTransformers` and `Region2VecExModel`. The first column of metadata file must match the BED file names -(the first column contains BED file names, or strings which BED file names start with), and is sorted by the first column. It can be -sorted by a terminal command: -``` -sort -k1 1 metadata_file > new_metadata_file -``` -Example code to build a list of RegionSetInfo - -```python -from geniml.text2bednn.utils import build_regionset_info_list_from_files -from geniml.region2vec.main import Region2VecExModel -from fastembed.embedding import FlagEmbedding - -# load Region2Vec from hugging face -r2v_model = Region2VecExModel("databio/r2v-ChIP-atlas") -# load natural language embedding model -nl_model = FlagEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2") -# folder of bed file -bed_folder = "path/to/folders/of/bed/files" -# path for metadata file -metadata_path = "path/to/file/of/metadata" - -# list of RegionSetInfo -ri_list = build_regionset_info_list_from_files(bed_folder, metadata_path, r2v_model, nl_model) -``` +`Vec2VecFNN`, a feedforward neural network (FNN), is trained to maps vectors from the embedding space of natural language to the embedding +space of BED files. When a natural language query string is given, it will first be encoded to a vector by the text embedding model, and that +vector will be encoded to a query vector by the FNN. `search` backend can perform k-nearest neighbors (KNN) search among the stored BED +file embedding vectors, and the BED files whose embedding vectors are closest to that query vector are the search results. -## Upload metadata and regions from PEP -A list of RegionSetInfo can also be created with a [`PEP`](https://pep.databio.org/en/latest/), which includes a `.csv` that stores metadata, and a `.yaml` as a a metadata validation -framework. - -Example code to build a list of RegionSetInfo from a PEP: - -```python -from geniml.text2bednn.utils import build_regionset_info_list_from_PEP - -# columns in the csv of PEP that contains metadata information -columns = return [ - "tissue", - "cell_line", - "tissue_lineage", - "tissue_description", - "diagnosis", - "sample_name", - "antibody", - ] - -# path to the yaml file -yaml_path = "path/to/framework/yaml/file" - -ri_list_PEP = build_regionset_info_list_from_PEP( - yaml_path, - col_names, - r2v_model, - nl_model, - ) -``` +## Store embedding vectors +It is recommended to use `geniml.search.backend.HNSWBackend` to store embedding vectors. In the `HNSWBackend` that stores each BED file embedding +vector, the `payload` should contain the name of BED file. In the `HNSWBackend` that stores the embedding vectors of each +metadata string, the `payload` should contain the name of BED files that have that string in metadata. ## Train the model -The list of RegionSetInfo can be split into 3 lists, which represent the training set, validating set, and testing set. The embedding -vectors of metadata will be X, and the embedding vectors of the region set will be Y. +Training a `Vec2VecFNN` needs x-y pairs of vectors (x: metadata embedding vector; y: BED embedding vector). A pair of a metadata embedding +vector with the embedding vectors of BED files in its payload is a target pair, othersie a non-target pair. Non-target pairs are sampled for +contrastive loss. Here is sample code to generate pairs from storage backend and train the model: ```python -from sklearn.model_selection import train_test_split -from geniml.text2bednn.utils region_info_list_to_vectors -from geniml.text2bednn.text2bednn import Vec2VecFNN +# target is an array of 1 (target) and -1 (non-target) +X, Y, target = vec_pairs( + nl_backend, # HNSWBackend that store metadata embedding vectors + bed_backend, # HNSWBackend that store BED embedding vectors + "name", # key to file name in BED backend payloads + "files", # key to matching files in metadata backend payloads + True, # sample non-target pairs + 1.0 # number of non-target pairs /number of target pairs = 1 +) + +# train without validate data +v2v_torch_contrast.train( + X, + Y, + folder_path="path/to/folder/for/checkpoint", + loss_func="cosine_embedding_loss", # right now "cosine_embedding_loss" is the only contrastive loss function available + training_target=target, +) -# split the list of RegionInfoSet into different data set -train_list, validate_list = train_test_split(ri_list, test_size=0.2) - -# get the embedding vectors -train_X, train_Y = region_info_list_to_vectors(train_list) -validate_X, validate_Y = region_info_list_to_vectors(validate_list) - -# train the neural network -v2vnn = Vec2VecFNN() -v2vnn.train(train_X, train_Y, validating_data=(validate_X, validate_Y), num_epochs=50) -``` - -## Load the vectors and information to search backend -[`qdrant-client`](https://github.com/qdrant/qdrant-client) and [`hnswlib`](https://github.com/nmslib/hnswlib) can store vectors and perform k-nearest neighbors (KNN) search with a given query vector, so we -created one database backend (`QdrantBackend`) and one local file backend (`HNSWBackend`) that can store the embedding -vectors for KNN search. `HNSWBackend` will create a .bin file with given path, which saves the searching index. - -```python -from geniml.text2bednn.utils import prepare_vectors_for_database - -# loading data to search backend -embeddings, labels = prepare_vectors_for_database(ri_list) - -# search backend -hnsw_backend = HNSWBackend(local_index_path="path/to/local/index.bin") -hnsw_backend.load(embeddings, labels) ``` ## text2bednn search interface @@ -119,3 +54,26 @@ query_term = "human, kidney, blood" # perform KNN search with K = 5, the id of stored vectors and the distance / similarity score will be returned ids, scores = file_interface.nl_vec_search(query_term, 5) ``` + +### Evaluate search performance +With a dictionary that contains query strings and id of relevant query results in search backend in this format: +``` +{ + : [ + , + ... + ], + ... +} +``` +`TextToBedNNSearchInterface` can return [mean average precision](https://www.youtube.com/watch?v=pM6DJ0ZZee0&t=157s), [average AUC-ROC](https://nlp.stanford.edu/IR-book/pdf/08eval.pdf), and [average R-Precision](https://link.springer.com/referenceworkentry/10.1007/978-0-387-39940-9_491), here is example code: +```python +query_dict = { + "metadata string 1": [2, 3], + "metadata string 12": [1], + "metadata string 3": [2, 4, 5], + "metadata string 1": [0] +} + +MAP, AUC, RP = file_interface.eval(query_dict) +``` From f22128d75c29d1789b75f298efd3d9f5a24e10d2 Mon Sep 17 00:00:00 2001 From: Khoroshevskyi Date: Tue, 27 Feb 2024 22:36:04 +0100 Subject: [PATCH 16/17] updated bedboss docs 6 --- docs/bedboss/bedbase_configuration.yaml | 23 -------- docs/bedboss/tutorials/bedqc_tutorial.md | 33 ++++++++++- docs/bedboss/tutorials/bedstat_tutorial.md | 56 ++++++++++++++++++- docs/bedboss/tutorials/tutorial_all.md | 62 ++++++++++++++++++++- docs/bedboss/tutorials/tutorial_insert.md | 65 +++++++++++++++++++++- 5 files changed, 212 insertions(+), 27 deletions(-) delete mode 100644 docs/bedboss/bedbase_configuration.yaml diff --git a/docs/bedboss/bedbase_configuration.yaml b/docs/bedboss/bedbase_configuration.yaml deleted file mode 100644 index 081be1d..0000000 --- a/docs/bedboss/bedbase_configuration.yaml +++ /dev/null @@ -1,23 +0,0 @@ -path: - pipeline_output_path: $BEDBOSS_OUTPUT_PATH # do not change it - bedstat_dir: bedstat_output - remote_url_base: null - bedbuncher_dir: bedbucher_output -database: - host: localhost - port: 5432 - password: docker - user: postgres - name: pep-db - dialect: postgresql - driver: psycopg2 -server: - host: 0.0.0.0 - port: 8000 -remotes: - http: - prefix: https://data.bedbase.org/ - description: HTTP compatible path - s3: - prefix: s3://data.bedbase.org/ - description: S3 compatible path \ No newline at end of file diff --git a/docs/bedboss/tutorials/bedqc_tutorial.md b/docs/bedboss/tutorials/bedqc_tutorial.md index f32c8b8..3ff9a46 100644 --- a/docs/bedboss/tutorials/bedqc_tutorial.md +++ b/docs/bedboss/tutorials/bedqc_tutorial.md @@ -1 +1,32 @@ -### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file +## BEDqc + +BEDqc is a tool for quality control of BED files. +As for now, it checks: +- maximum file size, +- maximum number of regions, +- minimum region width threshold + +---- +### Run BEDqc from command line +```bash +bedboss qc \ + --bedfile path/to/bedfile.bed \ + --outfolder path/to/output/dir \ +``` + +--- + +Run BEDqc from within Python +```python +from bedboss import bedqc + +bedqc.run_bedqc( + bedfile="path/to/bedfile.bed", + outfolder="path/to/output/dir" + max_file_size=1000000, # optional + max_number_of_regions=1000, # optional + min_region_width=10, # optional +) +``` + +If file won't pass the quality control, it will raise an error. and add this information to the log file. \ No newline at end of file diff --git a/docs/bedboss/tutorials/bedstat_tutorial.md b/docs/bedboss/tutorials/bedstat_tutorial.md index f32c8b8..cc34146 100644 --- a/docs/bedboss/tutorials/bedstat_tutorial.md +++ b/docs/bedboss/tutorials/bedstat_tutorial.md @@ -1 +1,55 @@ -### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file +# BEDstats + +BEDstats is a tool that calculates the statistics of a BED file and provides plots to visualize the results. + +It produces BED file Statistics: + +- **GC content**.The average GC content of the region set. +- **Number of regions**. The total number of regions in the BED file. +- **Median TSS distance**. The median absolute distance to the Transcription Start Sites (TSS) +- **Mean region width**. The average region width of the region set. +- **Exon percentage**. The percentage of the regions in the BED file that are annotated as exon. +- **Intron percentage**. The percentage of the regions in the BED file that are annotated as intron. +- **Promoter proc percentage**. The percentage of the regions in the BED file that are annotated as promoter-prox. +- **Intergenic percentage**. The percentage of the regions in the BED file that are annotated as intergenic. +- **Promoter core percentage**. The percentage of the regions in the BED file that are annotated as promoter-core. +- **5' UTR percentage**. The percentage of the regions in the BED file that are annotated as 5'-UTR. +- **3' UTR percentage**. The percentage of the regions in the BED file that are annotated as 3'-UTR. + +--- + +### Step 1: Install all dependencies + +First you have to install bedboss and check if all requirements are satisfied. +To do so, you can run next command: +```bash +bedboss requirements-check +``` +If requirements are not satisfied, you will see the list of missing packages. + + +### Step 2: Run bedstats + +#### Run BEDstats from command line +```bash +bedboss stats \ + --bedfile path/to/bedfile.bed \ + --outfolder path/to/output/dir \ + --genome hg38 \ + --bigbed "path/to/bigbedfile.bigbed" # optional +``` + +---- +#### Run BEDstats from within Python +```python +from bedboss import bedstats + +bedstat( + bedfile="path/to/bedfile.bed", + outfolder="path/to/output/dir", + genome="hg19", + bigbed="path/to/bigbedfile.bigbed", # optional + ) +``` + +After running BEDstats, you will find the following files in the output directory + all statistics will be saved in output file. \ No newline at end of file diff --git a/docs/bedboss/tutorials/tutorial_all.md b/docs/bedboss/tutorials/tutorial_all.md index f32c8b8..fb7d23c 100644 --- a/docs/bedboss/tutorials/tutorial_all.md +++ b/docs/bedboss/tutorials/tutorial_all.md @@ -1 +1,61 @@ -### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file +## Bedboss all + +Bedboss run-all is intended to run on sample (bed file) and run all bedboss pipelines: + bedmaker (+ bedclassifier + bedqc) -> bedstat. After that optionally it can run bedbuncher, qdrant indexing and upload metadata to PEPhub. + +### Step 1: Install all dependencies + +First you have to install bedboss and check if all requirements are satisfied. +To do so, you can run next command: +```bash +bedboss requirements-check +``` +If requirements are not satisfied, you will see the list of missing packages. + +### Step 2: Create bedconf.yaml file +To run bedboss, you need to create a bedconf.yaml file with configuration. +Detail instructions are in the configuration section. + +### Step 3: Run bedboss +To run bedboss, you need to run the next command: +```bash +bedboss all \ + --bedbase-config bedconf.yaml \ + --input-file path/to/bedfile.bed \ + --output-dir path/to/output/dir \ + --input-type bed \ + --genome hg38 \ + +``` + +Above command will run bedboss on the bed file and create a bedstat file in the output directory. +It contains only required parameters. For more details, please check the usage section. + +By default, results will be uploaded only to postgres database. +- To upload results to PEPhub, you need to make `databio` org available on GitHub, then login to PEPhub, and add `--upload-pephub` flag to the command. +- To upload results to Qdrant, you need to add `--upload-qdrant` flag to the command. +- To upload actual files to s3, you need to add `--upload-s3` flag to the command, and Before uploading you have to set up all necessary env vars: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_ENDPOINT_URL. + + +--- + +### Run bedboss all from within Python + +To run bedboss all from within Python, instead of using the command line in the step #3, you can use the following code: + +```python +from bedboss import bedboss + +bedboss.run_all( + sample_name="sample1", + input_file="path/to/bedfile.bed", + input_type="bed", + outfolder="path/to/output/dir", + genome="hg38", + bedbase_config="bedconf.yaml", + narrowpeak=False, # optional + standardardize=True, # optional + other_metadata=None, # optional + upload_pephub=True, # optional +) +``` \ No newline at end of file diff --git a/docs/bedboss/tutorials/tutorial_insert.md b/docs/bedboss/tutorials/tutorial_insert.md index f32c8b8..2073bd3 100644 --- a/docs/bedboss/tutorials/tutorial_insert.md +++ b/docs/bedboss/tutorials/tutorial_insert.md @@ -1 +1,64 @@ -### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file +## Bedboss insert + +Bedboss insert is intended to run each sample in provided PEP. +PEP can be provided as a file or as a registry path of the PEPhub. + + +### Step 1: Install all dependencies + +First you have to install bedboss and check if all requirements are satisfied. +To do so, you can run next command: +```bash +bedboss requirements-check +``` +If requirements are not satisfied, you will see the list of missing packages. + +### Step 2: Create bedconf.yaml file +To run bedboss insert, you need to create a bedconf.yaml file with configuration. +Detail instructions are in the configuration section. + +### Step 3: Create PEP with bed files. +BEDboss PEP should contain next fields: sample_name, input_file, input_type, genome. +Before running bedboss, you need to validate provided PEP with [bedboss_insert schema](https://schema.databio.org/?namespace=pipelines&schema=bedboss). +The easiest way to do so is to use [PEPhub](https://pephub.databio.org/), where you create a new PEP and validate it with the schema. +Example PEP: [https://pephub.databio.org/databio/excluderanges?tag=bedbase](https://pephub.databio.org/databio/excluderanges?tag=bedbase) + +### Step 4: Run bedboss insert +To run bedboss insert , you need to run the next command: +```bash +bedboss insert \ + --bedbase-config bedconf.yaml \ + --pep path/to/pep.yaml \ + --output-folder path/to/output/dir + +``` + +Above command will run bedboss on the bed file and create a bedstat file in the output directory. +It contains only required parameters. For more details, please check the usage section. + +By default, results will be uploaded only to postgres database. +- To upload results to PEPhub, you need to make `databio` org available on GitHub, then login to PEPhub, and add `--upload-pephub` flag to the command. +- To upload results to Qdrant, you need to add `--upload-qdrant` flag to the command. +- To upload actual files to s3, you need to add `--upload-s3` flag to the command, and Before uploading you have to set up all necessary env vars: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_ENDPOINT_URL. +- To create bedset of provided pep files, you need to add `--create-bedset` flag to the command. + + +--- + +### Run bedboss insert from within Python + +To run bedboss insert from within Python, instead of using the command line in the step #4, you can use the following code: + +```python +from bedboss import bedboss + +bedboss.insert_pep( + bedbase_config="bedconf.yaml", + pep="path/to/pep.yaml", + output_folder="path/to/output/dir", + upload_pephub=True, # optional + upload_qdrant=True, # optional + upload_s3=True, # optional + create_bedset=True # optional +) +``` \ No newline at end of file From 52d518389eba2905a8c66080f8099fbe528e2336 Mon Sep 17 00:00:00 2001 From: Khoroshevskyi Date: Thu, 29 Feb 2024 18:20:58 +0100 Subject: [PATCH 17/17] Updated docs --- docs/bedboss/tutorials/bedmaker_tutorial.md | 40 ++++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/docs/bedboss/tutorials/bedmaker_tutorial.md b/docs/bedboss/tutorials/bedmaker_tutorial.md index f32c8b8..76a748f 100644 --- a/docs/bedboss/tutorials/bedmaker_tutorial.md +++ b/docs/bedboss/tutorials/bedmaker_tutorial.md @@ -1 +1,39 @@ -### 🚧 Tutorial in progress! Stay tuned for updates. We're working hard to bring you valuable content soon! \ No newline at end of file +## BEDmaker + +The BEDmaker is a tool that allows you to convert various file types into BED format and bigBed format. +Currently supported formats are: +- bed +- bigBed +- bigWig +- wig + +Before running pipeline first, you have to install bedboss and check if bedmaker requirements are satisfied. +To do so, you can run the next command: +```bash +bedboss requirements-check +``` + +### Run BEDmaker from command line +```bash +bedboss make \ + --input-file path/to/input/file \ + --input-type bed\ + --output-folder path/to/output/dir \ + --genome hg38 \ + --sample-name sample1 + --bigbed "path/to/bigbedfile.bigbed" # optional +``` + +### Run BEDmaker from within Python +```python +from bedboss.bedmaker.bedmaker import make_all + +make_all( + input_file="path/to/input/file", + input_type="bed", + output_folder="path/to/output/dir", + genome="hg38", + sample_name="sample1", + bigbed="path/to/bigbedfile.bigbed" # optional +) +``` \ No newline at end of file

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