If you use this work in published research, please cite:
Using clusterProfiler to characterise Multi-Omics Data
This repo contains source code and data to produce Figures of the above paper.
The IBD_2_subtypes_example, Phyllostachys_heterocyla_example and
single_cell_example contain the data, scripts and results of the three
examples in the above article. Each sub directory contains input_data,
result, script.
- input_data: contains all the data sets that used to generate the figures.
- result: contains the results.
- script: contains the source code to produce the figures.
More details information can be found from here.
Here is the output of sessionInfo() of the system was compiled:
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /mnt/d/UbuntuApps/R/4.3.0/lib/R/lib/libRblas.so
## LAPACK: /mnt/d/UbuntuApps/R/4.3.0/lib/R/lib/libRlapack.so; LAPACK version 3.11.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Asia/Shanghai
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gridExtra_2.3 patchwork_1.2.0
## [3] ggsc_1.1.4 ggrepel_0.9.3
## [5] CelliD_1.8.1 SingleCellExperiment_1.22.0
## [7] SeuratObject_4.1.3 Seurat_4.3.0
## [9] ggfun_0.1.5 DESeq2_1.40.1
## [11] SummarizedExperiment_1.30.1 Biobase_2.60.0
## [13] MatrixGenerics_1.12.0 matrixStats_0.63.0
## [15] GenomicRanges_1.52.0 GenomeInfoDb_1.36.0
## [17] IRanges_2.36.0 S4Vectors_0.40.2
## [19] BiocGenerics_0.48.1 aplot_0.2.2
## [21] dplyr_1.1.2 enrichplot_1.22.0
## [23] ggplot2_3.5.0 clusterProfiler_4.10.1
## [25] MicrobiotaProcess_1.15.0 tictoc_1.2.1
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.2 spatstat.sparse_3.0-1
## [3] bitops_1.0-7 HDO.db_0.99.1
## [5] httr_1.4.5 RColorBrewer_1.1-3
## [7] tools_4.3.0 sctransform_0.3.5
## [9] utf8_1.2.3 R6_2.5.1
## [11] vegan_2.6-4 lazyeval_0.2.2
## [13] uwot_0.1.14 mgcv_1.8-42
## [15] permute_0.9-7 withr_2.5.0
## [17] sp_1.6-0 progressr_0.13.0
## [19] cli_3.6.1 spatstat.explore_3.1-0
## [21] scatterpie_0.2.2 sandwich_3.0-2
## [23] mvtnorm_1.1-3 spatstat.data_3.0-1
## [25] askpass_1.1 ggridges_0.5.4
## [27] pbapply_1.7-0 yulab.utils_0.1.4
## [29] gson_0.1.0 DOSE_3.26.1
## [31] scater_1.28.0 parallelly_1.35.0
## [33] RSQLite_2.3.1 generics_0.1.3
## [35] gridGraphics_0.5-1 ica_1.0-3
## [37] spatstat.random_3.1-5 GO.db_3.17.0
## [39] Matrix_1.5-4 ggbeeswarm_0.7.2
## [41] fansi_1.0.4 abind_1.4-5
## [43] lifecycle_1.0.3 multcomp_1.4-25
## [45] yaml_2.3.7 qvalue_2.32.0
## [47] SparseArray_1.2.4 Rtsne_0.16
## [49] grid_4.3.0 blob_1.2.4
## [51] promises_1.2.0.1 crayon_1.5.2
## [53] miniUI_0.1.1.1 lattice_0.21-8
## [55] beachmat_2.19.1 cowplot_1.1.1
## [57] KEGGREST_1.40.0 pillar_1.9.0
## [59] knitr_1.43 fgsea_1.26.0
## [61] future.apply_1.10.0 codetools_0.2-19
## [63] fastmatch_1.1-3 leiden_0.4.3
## [65] glue_1.6.2 RcppArmadillo_0.12.2.0.0
## [67] data.table_1.14.8 vctrs_0.6.3
## [69] png_0.1-8 treeio_1.27.0
## [71] gtable_0.3.3 cachem_1.0.8
## [73] xfun_0.39 S4Arrays_1.3.3
## [75] mime_0.12 libcoin_1.0-9
## [77] tidygraph_1.2.3 survival_3.5-5
## [79] iterators_1.0.14 ellipsis_0.3.2
## [81] fitdistrplus_1.1-11 TH.data_1.1-2
## [83] ROCR_1.0-11 nlme_3.1-162
## [85] ggtree_3.9.1 bit64_4.0.5
## [87] RcppAnnoy_0.0.20 irlba_2.3.5.1
## [89] vipor_0.4.5 KernSmooth_2.23-22
## [91] colorspace_2.1-0 DBI_1.1.3
## [93] tidyselect_1.2.0 bit_4.0.5
## [95] compiler_4.3.0 BiocNeighbors_1.18.0
## [97] DelayedArray_0.29.4 plotly_4.10.1
## [99] shadowtext_0.1.2 scales_1.3.0
## [101] lmtest_0.9-40 stringr_1.5.0
## [103] digest_0.6.33 goftest_1.2-3
## [105] spatstat.utils_3.0-3 rmarkdown_2.22
## [107] XVector_0.40.0 htmltools_0.5.5
## [109] pkgconfig_2.0.3 umap_0.2.10.0
## [111] sparseMatrixStats_1.12.0 fastmap_1.1.1
## [113] rlang_1.1.1 htmlwidgets_1.6.2
## [115] shiny_1.7.4 DelayedMatrixStats_1.22.0
## [117] farver_2.1.1 zoo_1.8-12
## [119] jsonlite_1.8.7 BiocParallel_1.34.2
## [121] GOSemSim_2.27.2 BiocSingular_1.16.0
## [123] RCurl_1.98-1.12 magrittr_2.0.3
## [125] modeltools_0.2-23 scuttle_1.10.1
## [127] GenomeInfoDbData_1.2.10 ggplotify_0.1.0
## [129] munsell_0.5.0 Rcpp_1.0.10
## [131] ape_5.7-1 ggnewscale_0.4.9
## [133] viridis_0.6.2 reticulate_1.28
## [135] stringi_1.7.12 ggstar_1.0.4.001
## [137] ggraph_2.1.0 zlibbioc_1.46.0
## [139] MASS_7.3-59 plyr_1.8.8
## [141] parallel_4.3.0 listenv_0.9.0
## [143] deldir_1.0-6 Biostrings_2.68.1
## [145] graphlayouts_1.0.0 splines_4.3.0
## [147] tensor_1.5 locfit_1.5-9.7
## [149] igraph_1.4.2 spatstat.geom_3.2-1
## [151] ggtreeExtra_1.11.0 ggsignif_0.6.4
## [153] ScaledMatrix_1.8.1 reshape2_1.4.4
## [155] evaluate_0.21 RcppParallel_5.1.7
## [157] foreach_1.5.2 tweenr_2.0.2
## [159] httpuv_1.6.11 openssl_2.0.6
## [161] RANN_2.6.1 tidyr_1.3.0
## [163] purrr_1.0.1 polyclip_1.10-4
## [165] future_1.32.0 scattermore_0.8
## [167] ggforce_0.4.1 rsvd_1.0.5
## [169] coin_1.4-2 xtable_1.8-4
## [171] RSpectra_0.16-1 tidytree_0.4.5
## [173] tidydr_0.0.5 later_1.3.1
## [175] viridisLite_0.4.2 tibble_3.2.1
## [177] beeswarm_0.4.0 memoise_2.0.1
## [179] AnnotationDbi_1.62.1 cluster_2.1.4
## [181] globals_0.16.2