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initial_analysis.Rmd
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initial_analysis.Rmd
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---
title: "PTSD oral microbiome report"
author: "Guy Shapira"
date: "2/13/2021"
output:
html_document:
df_print: paged
toc: true
toc_float: true
---
```{r}
knitr::opts_chunk$set(echo = F, warning = F, message = F)
knitr::opts_chunk$set(fig.width=12, fig.height=8)
library(tidyverse)
library(ggrepel)
library(phyloseq)
library(microbiome)
```
```{r}
# Air pollution
df=readxl::read_excel("city_level_SPSS.xlsx")
df=df[,c("Subject ID", "PM2.5 (μg/m^3)")]
df=df %>% dplyr::rename(ID="Subject ID")
df$ID=str_pad(df$ID, 4, side = "left", pad = "0")
sam=sam %>% left_join(df, by="ID")
```
```{r}
sam$zipcode=str_trim(sam$zipcode)
```
```{r}
sam %>% write_csv("samples.csv")
```
```{r}
saveRDS(sam, "revised_samplesheet.rds")
```
```{r}
sam=read_delim("sample-new-metadata.tsv", "\t")
# Remove qiime2 format line
var_cat=sam[1,]
sam=sam[-1,]
```
# Metadata processing
```{r eval=FALSE}
# Adding extra variables
# Positive parameters
df=readxl::read_excel("PTSD_positive variables_5waves.xlsx")
df$ID=str_pad(df$ID, 4, side = "left", pad = "0")
sam=sam %>% left_join(df[, !(colnames(df) %in% c("npt_total_5waves", "Diag_score_5waves"))], by="ID")
# Drug use parameters
df=readxl::read_excel("medicine file_CSR study.xlsx")
cdf=readxl::read_excel("medicine file_CSR study.xlsx", sheet = 2, col_names = c("code", "Medication"))
df$ID=str_pad(df$ID, 4, side = "left", pad = "0")
for(c in cdf$code){
mname=str_c("Drug ", cdf[cdf$code == c,]$Medication)
ddf=tibble(ID=df$ID)
ddf[[mname]]=unlist(lapply(df$ID, function(x) c %in% df[df$ID == x,][,-1]))
sam=sam %>% left_join(ddf, by="ID")
}
# Add 5-wave data
df=readxl::read_excel("5 waves.xlsx")
df$ID=str_pad(df$ID, 4, side = "left", pad = "0")
sam$ID=str_pad(sam$ID, 4, side = "left", pad = "0")
sam=sam %>% dplyr::select(-`npt83`, -`npt84`, -`npt85`, -`npt2002`, -`npt17`) %>% left_join(df, by="ID")
sam %>% write_delim("sample-metadata.tsv", "\t")
sam %>% count(health_group)
```
```{r}
sam %>% ggplot(aes(x=Total_severity, fill=NEW_PTSD2017DSM5N)) + geom_histogram() + geom_vline(xintercept = 2.7)
sam %>% ggplot(aes(x=Total_severity, fill=NEW_PTSD2017DSM5Distress)) + geom_histogram() + geom_vline(xintercept = 2.7)
sam %>% ggplot(aes(x=Total_severity, fill=NEW_PTSD2017DSM5fDiss)) + geom_histogram() + geom_vline(xintercept = 2.7)
sam %>% ggplot(aes(x=Total_severity, fill=NEW_PTSD2017DSM5fReal)) + geom_histogram() + geom_vline(xintercept = 2.7)
```
```{r}
sam$extreme=NA
ss=sam %>% dplyr::filter((Total_severity > 2.7) & NEW_PTSD2017DSM5N & (group == "Combat stress in 1983") & current_PTSD)
sam=sam %>% mutate(extreme=ifelse(ID %in% ss$ID, "PTSD", extreme))
```
```{r}
ss=sam %>% dplyr::filter(Total_severity < 1.1)
sam=sam %>% mutate(extreme=ifelse(ID %in% ss$ID, "Control", extreme))
sam=sam %>% mutate(extreme=ifelse(is.na(extreme), "non_extreme", extreme))
```
```{r}
# Heatmap annotations
library(ComplexHeatmap)
#Age=sam$Age,
#CriterionG_distress=sam$CriterionG_distress, CriterionG_functioning=sam$CriterionG_functioning,
# hypothetical_batch=sam$hypothetical_batch,
bin_params_colorscheme=c("FALSE"="blue", "TRUE"="red")
ha=HeatmapAnnotation(extreme=sam$extreme,
current_PTSD=sam$current_PTSD,
allergy=sam$allergy,
Blood_preasure=sam$Blood_preasure,
Ulcer=sam$Ulcer,
other_digestion_issues =sam$other_digestion_issues,
heart_problems=sam$heart_problems,
chest_pain=sam$chest_pain,
Diabetes=sam$Diabetes,
back_ache=sam$back_ache,
significant_recent_weight_gain=sam$significant_recent_weight_gain,
significant_recent_weight_loss=sam$significant_recent_weight_loss,
Head_aches=sam$Head_aches,
joint_aches =sam$joint_aches ,
memory_difficulties=sam$memory_difficulties,
Weakness_Fatigue=sam$Weakness_Fatigue,
cancer=sam$cancer,
chronic_pained_non_idiopathic=sam$chronic_pained_non_idiopathic,
chronic_idiopathic_pain=sam$chronic_idiopathic_pain,
drug_abuse=sam$drug_abuse,
regular_medication=sam$regular_medication,
alcohol_abuse=sam$alcohol_abuse,
Smoking=sam$Smoking,
col = list(extreme=c("Control"="blue", "non_extreme"="gray", "PTSD"="red"),
condition=bin_params_colorscheme, current_PTSD=bin_params_colorscheme,
Blood_preasure=bin_params_colorscheme,
Ulcer=bin_params_colorscheme,
other_digestion_issues =bin_params_colorscheme,
heart_problems=bin_params_colorscheme,
chest_pain=bin_params_colorscheme,
Diabetes=bin_params_colorscheme,
back_ache=bin_params_colorscheme,
significant_recent_weight_gain=bin_params_colorscheme,
significant_recent_weight_loss=bin_params_colorscheme,
Head_aches=bin_params_colorscheme,
joint_aches =bin_params_colorscheme,
memory_difficulties=bin_params_colorscheme,
Weakness_Fatigue=bin_params_colorscheme,
cancer=bin_params_colorscheme,
chronic_pained_non_idiopathic=bin_params_colorscheme,
chronic_idiopathic_pain=bin_params_colorscheme,
allergy=bin_params_colorscheme,
drug_abuse=bin_params_colorscheme,
regular_medication=bin_params_colorscheme,
alcohol_abuse=bin_params_colorscheme,
Smoking=bin_params_colorscheme), show_legend = F)
```
Strong clustering seperates the cohort into two groups, with the PTSD group associated with increased occurrence of comorbidities and worse psychiatric metrics overall.
```{r}
mat=sam %>% dplyr::select(where(is.numeric)) %>% as.data.frame() %>% as.matrix()
rownames(mat)=sam$ID
mat=t(scale(mat))
Heatmap(mat, name = "Z-score", top_annotation = ha)
```
```{r eval=FALSE}
library(rstatix)
param="extreme"
ndigits=1
lpack=c("Age", "npt17_aroReac_DSM4_5", "npt17_avoDSM4_5", "npt17_intDSM4_5", "npt17_negCogmoodDSM5", "scl_tot", "scl2017_ADDITIONAL", "scl2017_ANXIETY", "scl2017_gsi", "scl2017_HOSTILITY", "scl2017_OBSESSION", "scl2017_PARANOIA", "scl2017_PHOBIA", "scl2017_PSYCHOT", "scl2017_SENSITIVITY", "scl2017_SOMATIC")
lpack=cont_params
rsam=sam %>% dplyr::filter(is.na(Ulcer))
df=rsam %>% group_by(!!rlang::sym(param)) %>%
get_summary_stats() %>%
mutate(range=str_c(signif(round(`min`, ndigits), ndigits), " - ",
signif(round(`max`, ndigits), ndigits)),
`mean (sd)`=str_c(signif(round(`mean`, ndigits), ndigits),
" ± ",
signif(round(`sd`, ndigits), ndigits)),
parameter=!!rlang::sym(param)) %>%
dplyr::select(parameter, `variable`, range, `mean (sd)`) %>%
pivot_longer(c(`range`, `mean (sd)`)) %>%
pivot_wider(names_from=parameter, values_from=`value`)
ddf=tibble()
for(p in lpack){
ddf=ddf %>% bind_rows(rsam %>% t_test(as.formula(str_c(p, "~", param))))
}
ddf=ddf %>% mutate(p=signif(`p`, ndigits), statistic=str_c("t=", signif(`statistic`, ndigits))) %>% dplyr::select(`.y.`, `statistic`, `p`) %>% dplyr::rename(`variable`=".y.")
df=df %>% left_join(ddf, by="variable")
# Binary parameters
ptt=c("hypothetical_batch","previously_sequenced","group","current_PTSD","CriterionG_distress","CriterionG_functioning","allergy","Blood_preasure","Ulcer","other_digestion_issues","heart_problems","chest_pain","Diabetes","back_ache","significant_recent_weight_gain","significant_recent_weight_loss","Head_aches","joint_aches","memory_difficulties","Weakness_Fatigue","cancer","chronic_pained_non_idiopathic","chronic_idiopathic_pain","drug_abuse","regular_medication","alcohol_abuse","Smoking")
ptt=ptt[ptt != param]
rdf=tibble()
for(pt in ptt)
{
rdf=rdf %>% bind_rows(rsam %>% dplyr::filter(!is.na(!!rlang::sym(pt))) %>% group_by(!!rlang::sym(param)) %>% dplyr::count(!!rlang::sym(pt)) %>% pivot_wider(id_cols = !!rlang::sym(pt), names_from=!!rlang::sym(param), values_from=`n`) %>% mutate(variable=as.character(pt)) %>% dplyr::rename(name=pt) %>% mutate(name=as.character(name)))
}
rddf=tibble()
for(pt in ptt)
{
rddf=rddf %>% bind_rows(fisher_test(xtabs(as.formula(str_c("~", pt, "+", param)), rsam), detailed = T) %>% mutate(variable=pt))
}
rddf=rddf %>% mutate(p=signif(`p`, ndigits), statistic=str_c("X=", signif(`estimate`, ndigits))) %>% dplyr::select(`variable`, `statistic`, `p`)
rdf=rdf %>% left_join(rddf, by="variable")
df=df %>% bind_rows(rdf %>% mutate(`FALSE`=as.character(`FALSE`), `TRUE`=as.character(`FALSE`)) %>%
dplyr::select(`variable`, `name`, `FALSE`, `TRUE`, `statistic`, `p`))
library(flextable)
df %>%
dplyr::select(`variable`, `name`, `FALSE`, `TRUE`, `statistic`, `p`) %>%
flextable() %>% merge_v(j=~variable, target = c("variable", "statistic", "p")) %>%
theme_box()
```
# Circle cor-plot
```{r}
#c("ID","condition","health_group","hypothetical_batch","previously_sequenced","group","Birth_country","birth","Age","zipcode","current_PTSD","npt17_intDSM4_5","npt17_avoDSM4_5","npt17_negCogmoodDSM5","npt17_aroReac_DSM4_5","CriterionG_distress","CriterionG_functioning","scl_tot","scl2017_gsi","scl2017_SOMATIC","scl2017_OBSESSION","scl2017_SENSITIVITY","scl2017_ANXIETY","scl2017_HOSTILITY","scl2017_PHOBIA","scl2017_PARANOIA","scl2017_PSYCHOT","scl2017_ADDITIONAL","scl_unknown","allergy","Blood_preasure","Ulcer","other_digestion_issues","heart_problems","chest_pain","Diabetes","back_ache","significant_recent_weight_gain","significant_recent_weight_loss","Head_aches","joint_aches","memory_difficulties","Weakness_Fatigue","cancer","chronic_pained_non_idiopathic","chronic_idiopathic_pain","drug_abuse","regular_medication","alcohol_abuse","Smoking","familial_status","years_of_education","income","int_severity","neg_severity","react_severity","avo_severity","Total_severity","NEW_PTSD2017DSM5N","NEW_PTSD2017DSM5Distress","NEW_PTSD2017DSM5fDiss","NEW_PTSD2017DSM5fReal","int_count","neg_count","react_count","avo_count","Total_count_17","npt_total_5waves","Diag_score_5waves","npt83","npt84","npt85","npt2002","npt17","diag83","diag84","diag85","diag02","diag17 (Current_PTSD)","DAS_Total","PTG_GSI","SOC_SUP_Index","SWL_GSI","MHI_GSI","Drug Blood presure","Drug High cholesterol","Drug Benign polip in the urine bladder","Drug Heart problems\ after heart attack","Drug Medically perscribed Cannabis","Drug Thyroid problems","Drug Allergy","Drug Diabetes","Drug Blood thinner","Drug Biological treatment for cancer","Drug SSRI","Drug Glaucoma","Drug Orology","Drug Oxazepam (Vaben)","Drug Pain killers-not specified","Drug Diuretic mediciane","Drug Bupropion(Vloboterin)","Drug Sleeping pill-not specified","Drug Colitis medication","Drug Nexium","Drug Medication with steroids","Drug Colicitsin (Gout)","Drug Kidney problems","Drug Clonazepam (Clonex)","Drug Anti heartburn medicine","extreme")
library(corrplot)
colnames(sam)
sam[,c("Age", "years_of_education", "scl_tot","scl2017_gsi","scl2017_SOMATIC","scl2017_OBSESSION","scl2017_SENSITIVITY","scl2017_ANXIETY","scl2017_HOSTILITY","scl2017_PHOBIA","scl2017_PARANOIA","scl2017_PSYCHOT", "int_severity","neg_severity","react_severity","avo_severity","Total_severity","int_count","neg_count","react_count","avo_count","Total_count_17","npt_total_5waves","Diag_score_5waves","DAS_Total","PTG_GSI","SOC_SUP_Index","SWL_GSI","MHI_GSI")]
corrplot(cor(sam[,c("Age", "years_of_education", "scl_tot","scl2017_gsi","scl2017_SOMATIC","scl2017_OBSESSION","scl2017_SENSITIVITY","scl2017_ANXIETY","scl2017_HOSTILITY","scl2017_PHOBIA","scl2017_PARANOIA","scl2017_PSYCHOT", "int_severity","neg_severity","react_severity","avo_severity","Total_severity","int_count","neg_count","react_count","avo_count","Total_count_17","npt_total_5waves","Diag_score_5waves","DAS_Total","PTG_GSI","SOC_SUP_Index","SWL_GSI","MHI_GSI")], use = "na.or.complete"))
cat(str_c(colnames(sam), collapse = '","'))
```
# Microbiome pre-processing
```{r}
library(qiime2R)
ds=qza_to_phyloseq(features = "unfiltered-table.qza", tree = "rooted-tree.qza", taxonomy = "taxonomy.qza", metadata = "sample-new-metadata.tsv")
o=sam %>% as.data.frame()
rownames(o)=o$ID
sample_data(ds)=o
rs=rank_names(ds)[-1]
```
```{r}
rank_names(ds)
```
```{r}
unfiltered_dataset=ds
```
# Filtering
## Kingdom
```{r}
ds %>% aggregate_taxa("Kingdom") %>% plot_bar(x="Kingdom")
```
The Archea detected is of species typical to the oral cavity and likely to be correctly classified, however, they are too few.
All non-bacterial taxa are removed.
```{r}
ds=subset_taxa(ds, Kingdom == "Bacteria")
```
## Phylum
```{r}
ds %>% aggregate_taxa("Phylum") %>% transform_sample_counts(function(x)x/sum(x)) %>% plot_bar(fill="Phylum")
```
Sample 3230 is dominated by Proteobacteria and is a clear outlier when compared with the other samples, it is therefore removed from analysis.
```{r}
sam=sam[sam$ID != "3230",]
ds=prune_samples(sam$ID, ds)
```
```{r}
dds=ds %>% aggregate_taxa("Phylum")
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=Phylum)) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
We get rid of low-expression and uncharacterized phylums.
```{r}
ds=subset_taxa(ds, !is.na(Phylum) & !Phylum %in% c("", "uncharacterized"))
ds=subset_taxa(ds, ! Phylum %in% c("Unknown", "Chloroflexi", "Cyanobacteria"))
```
```{r}
dds=ds %>% aggregate_taxa("Phylum")
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=Phylum)) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
```{r}
rds=ds %>% tax_glom("Phylum", NArm = T)
taxa_names(rds) = as.data.frame(tax_table(rds)[rds %>% taxa_names(),])$Phylum
rds %>%
plot_tree("treeonly", nodeplotblank, ladderize = "left", label.tips = "taxa_names")
```
## Class
```{r}
trank="Class"
```
```{r}
ds %>% aggregate_taxa(trank) %>% transform_sample_counts(function(x)x/sum(x)) %>% plot_bar(fill=trank)
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
Get rid of low-expression and uncharacterized.
```{r}
ds=subset_taxa(ds, (!is.na(Class)) & (!(Class %in% c("", "uncharacterized"))))
ds=subset_taxa(ds, !(Class %in% prevdf[prevdf$TotalAbundance < 150,][,trank]))
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
```{r}
rds=ds %>% tax_glom(trank, NArm = T)
taxa_names(rds) = as.data.frame(tax_table(rds)[rds %>% taxa_names(),])[,trank]
rds %>%
plot_tree("treeonly", nodeplotblank, ladderize = "left", label.tips = "taxa_names")
```
## Order
```{r}
trank="Order"
```
```{r}
ds %>% aggregate_taxa(trank) %>% transform_sample_counts(function(x)x/sum(x)) %>% plot_bar(fill=trank)
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
Get rid of low-expression and uncharacterized.
```{r}
ds=subset_taxa(ds, (!is.na(Order)) & (!(Order %in% c("", "uncharacterized"))))
ds=subset_taxa(ds, !(Order %in% prevdf[prevdf$TotalAbundance < 150,][,trank]))
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
```{r}
rds=ds %>% tax_glom(trank, NArm = T)
taxa_names(rds) = as.data.frame(tax_table(rds)[rds %>% taxa_names(),])[,trank]
rds %>%
plot_tree("treeonly", nodeplotblank, ladderize = "left", label.tips = "taxa_names")
```
## Family
```{r}
trank="Family"
```
```{r}
ds %>% aggregate_taxa(trank) %>% transform_sample_counts(function(x)x/sum(x)) %>% plot_bar(fill=trank)
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
Get rid of low-expression and uncharacterized.
```{r}
ds=subset_taxa(ds, (!is.na(Family)) & (!(Family %in% c("", "uncharacterized"))))
ds=subset_taxa(ds, !(Family %in% prevdf[prevdf$TotalAbundance < 150,][,trank]))
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
```{r}
rds=ds %>% tax_glom(trank, NArm = T)
taxa_names(rds) = as.data.frame(tax_table(rds)[rds %>% taxa_names(),])[,trank]
rds %>%
plot_tree("treeonly", nodeplotblank, ladderize = "left", label.tips = "taxa_names")
```
## Genus
```{r}
trank="Genus"
```
```{r}
ds %>% aggregate_taxa(trank) %>% transform_sample_counts(function(x)x/sum(x)) %>% plot_bar(fill=trank)
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
Get rid of low-expression and uncharacterized.
```{r}
#prevdf[prevdf$TotalAbundance < 100,][,trank]
ds=subset_taxa(ds, (!is.na(Genus)) & (!(Genus %in% c("", "uncharacterized"))))
ds=subset_taxa(ds, !(Genus %in% prevdf[prevdf$TotalAbundance < 100,][,trank]))
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
```{r}
rds=ds %>% tax_glom(trank, NArm = T)
taxa_names(rds) = as.data.frame(tax_table(rds)[rds %>% taxa_names(),])[,trank]
rds %>%
plot_tree("treeonly", nodeplotblank, ladderize = "left", label.tips = "taxa_names")
```
## Species
```{r}
trank="Species"
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
Get rid of low-expression and uncharacterized.
```{r}
ds=subset_taxa(ds, (!is.na(Species)) & (!(Species %in% c("", "uncharacterized"))))
ds=subset_taxa(ds, !(Species %in% prevdf[prevdf$TotalAbundance < 100,][,trank]))
```
```{r}
dds=ds %>% aggregate_taxa(trank)
prevdf=apply(otu_table(dds),
ifelse(taxa_are_rows(dds), yes = 1, no = 2),
function(x) sum(x>0))
prevdf=data.frame(Prevalence=prevdf,
TotalAbundance=taxa_sums(dds),
tax_table(dds))
prevdf %>% ggplot(aes(TotalAbundance, Prevalence/nsamples(dds), label=!!rlang::sym(trank))) +
geom_point() + geom_text_repel() + theme_light() + scale_x_log10() +
ylab("Found in fraction of samples") + xlab("Total abundance")
```
```{r}
filtered_dataset=ds
```
```{r}
saveRDS(unfiltered_dataset, "unfiltered_dataset.rds")
saveRDS(filtered_dataset, "filtered_dataset.rds")
```
# Viz
```{r}
sam %>% dplyr::select(colnames(sam)[str_detect(colnames(sam), "Drug")]) %>%
pivot_longer(everything()) %>% dplyr::filter(value) %>% count(name) %>%
arrange(-n)
```
```{r}
res=read_csv("Drug.Diabetes.differential_abundance.csv")
res
```
```{r fig.height=8}
res=lapply(bracer::glob("differential_pathway_abundance/*.csv"),
function(x) read_csv(x) %>% mutate(comparison=str_replace(basename(x), ".pathway.csv", ""))) %>%
bind_rows()
res=lapply(bracer::glob("differential_abundance_results/*.ancomb.csv"),
function(x) read_csv(x) %>% mutate(comparison=str_replace(basename(x), ".ancomb.csv", ""))) %>%
bind_rows()
r=res %>% dplyr::filter(qval < 0.05) %>% mutate(trend=ifelse(stat > 0, "Increase",
"Decrease")) %>%
group_by(trend) %>%
count(comparison) %>% arrange(-n) %>%
left_join(res %>% dplyr::filter(qval < 0.05) %>% count(comparison) %>% rename(total="n"), by="comparison")
r$trend=factor(r$trend, levels = c("Increase","Decrease"))
r %>% ggplot(aes(reorder(comparison, total), n, group=comparison, fill=trend)) +
geom_bar(stat="identity") + theme_minimal() +
coord_flip() +
xlab("Significant differential abundance") +
scale_fill_manual(values = c("red", "blue")) +
theme(axis.text.x = element_text(angle = 90, size = 14, hjust = 1),
axis.title.y = element_blank())
```
```{r fig.height=8}
res %>% dplyr::filter((comparison == "Total_count_17") & (qval < 0.06)) %>%
distinct(taxon_rank, taxon) %>% left_join(res, by=c("taxon", "taxon_rank")) %>%
filter(qval < 0.06) %>% mutate(trend=ifelse(stat > 0, "up", "down")) %>%
ggplot(aes(taxon, comparison, color=trend)) +
geom_point() + theme_minimal() +
theme(axis.text.x = element_text(angle=90))
```
```{r}
res %>% dplyr::filter(comparison == "avo_count") %>% arrange(qval)
```
```{r fig.height=8}
library(ggradar)
colnames(sam)[str_detect(colnames(sam), "count")]
res %>% dplyr::filter(taxon %in% c("Bacteroidetes")) %>%
dplyr::filter(comparison %in% c("avo_count", "react_count", "neg_count", "int_count")) %>%
dplyr::select(taxon, comparison, stat) %>% pivot_wider(id_cols=1:3, names_from=comparison, values_from = stat) %>%
ggradar(grid.max = 4, centre.y = -4)
```
```{r}
cnts=ds %>%
subset_taxa(Phylum %in% c("Bacteroidetes", "Firmicutes", "Actinobacteria")) %>%
aggregate_taxa("Phylum") %>% transform_sample_counts(function(x) x/sum(x)) %>%
otu_table() %>% as.data.frame() %>% t() %>% as.data.frame()
r1=cnts %>% mutate(ID=rownames(cnts)) %>% tibble() %>%
pivot_longer(cols = -`ID`, names_to="Taxon", values_to = "fraction") %>%
left_join(sam, by="ID") %>% group_by(extreme) %>%
summarise(total=sum(fraction))
cnts %>% mutate(ID=rownames(cnts)) %>% tibble() %>%
pivot_longer(cols = -`ID`, names_to="Taxon", values_to = "fraction") %>%
left_join(sam, by="ID") %>% group_by(extreme, Taxon) %>%
summarise(frac=sum(fraction)) %>% left_join(r1, by="extreme") %>%
mutate(fraction=frac/total) %>% dplyr::filter(Taxon=="Bacteroidetes") %>%
ggplot(aes(extreme, fraction, fill=Taxon)) + geom_bar(stat = "identity")
ds %>% aggregate_taxa("Phylum") %>%
transform_sample_counts(function(x) x/sum(x)) %>%
plot_bar(x="extreme", fill="Phylum")
cnts=ds %>% aggregate_taxa("Phylum") %>% otu_table() %>% as.data.frame()
cnts=cnts["Bacteroidetes",]
rcnts=ds %>% aggregate_taxa("Class") %>% otu_table() %>% as.data.frame()
rcnts=rcnts["Negativicutes",]
cnts=cbind(t(cnts), t(rcnts)) %>% as.data.frame()
cnts$ID=rownames(cnts)
cnts=cnts %>% tibble()
cnts %>% left_join(sam, by="ID") %>%
pivot_longer(1:2, names_to = "Taxon", values_to = "Abundance") %>%
group_by()
%>%
ggplot(aes(extreme, Abundance, fill=Taxon)) + geom_bar(stat = "identity")
```
```{r}
res=read_csv("differential_abundance_results/extreme_PTSD_vs_Control.csv")
res
```
```{r}
# Construct phylogenetic tree based on results
```
```{r fig.height=8}
r=res %>% dplyr::filter(comparison %in% c("avo_count", "react_count", "neg_count", "int_count", "Total_count_17")) %>%
dplyr::filter(qval < 0.06) %>% dplyr::filter(taxon_rank=="Species") %>% distinct(taxon)
rds=ds %>% subset_taxa(Species %in% r$taxon) %>% tax_glom("Species")
o=phy_tree(rds)
# Annotate tips
oo=as.data.frame(tax_table(rds))$Species
names(oo)=rownames(tax_table(rds))
o$tip.label=unlist(lapply(o$tip.label, function(x) oo[[x]]))
library(ggtree)
o %>% ggtree() + layout_circular() +
geom_tiplab()
#geom_nodepoint(aes(color=))
```
```{r}
ttree=tax_table(rds) %>% as.data.frame() %>% tibble()
# Construct tax ID assignment
taxdict=ttree %>%
pivot_longer(everything(), names_to = "rank", values_to = "label") %>%
distinct(rank, label)
taxdict=taxdict %>% mutate(node=rownames(taxdict))
rs=c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
o=tibble()
for(trank in 1:(length(rs)-2)){
p1=rs[length(rs) - (trank + 1)]
p2=rs[length(rs) - trank]
# Add child and parent IDs
bb=c("label")
names(bb)=p2
r=ttree %>% distinct(!!rlang::sym(p2), !!rlang::sym(p1)) %>%
left_join(taxdict %>% dplyr::filter(rank == p2), by=bb)
bb=c("label")
names(bb)=p1
r=r %>%
left_join(taxdict %>% dplyr::filter(rank == p1) %>%
dplyr::rename(parent="node") %>% dplyr::select(-rank), by=bb)
# Add labels
r=r %>% mutate(label=!!rlang::sym(p2))
o=o %>% bind_rows(r %>% dplyr::select(parent, node, label))
}
o=o %>% dplyr::filter(!(parent %in% unique(o$node))) %>% mutate(node=parent) %>%
dplyr::select(-label) %>%
left_join(taxdict %>% dplyr::select(-rank), by="node") %>%
bind_rows(o)
o=o %>% mutate_at(c("parent", "node"), as.integer) %>%
dplyr::select(parent, node, label)
class(o)=c("tbl_tree", class(o))
o=o %>% tidytree::as.phylo()
ttree %>% distinct(Kingdom) %>% dplyr::rename(label="Kingdom") %>%
left_join( , by="label")
taxdict %>% dplyr::filter(rank == "Kingdom")
taxa_names(aggregate_taxa(rds, "Genus"))
phy_tree(rds) %>% tidytree::as_tibble() %>% tidytree::as.phylo()
```
```{r}
p1="Genus"
p2="Species"
ttree=tax_table(rds) %>% as.data.frame() %>% tibble() %>%
distinct(!!rlang::sym(p1), !!rlang::sym(p2)) %>%
dplyr::rename(parent=p1, node=p2) %>%
mutate(isTip=TRUE)
ttree=ttree %>% bind_rows(tibble(parent=unique(ttree$parent), node=unique(ttree$parent),
isTip=FALSE))
ttree=ttree %>%
mutate(branch.length=1, label=node)
ttree %>% dplyr::select(parent, node, branch.length, label, isTip) %>%
tidytree::as.phylo()
ds %>% subset_taxa(Phylum == "Bacteroidetes") %>%
phy_tree() %>% tidytree::as_tibble() %>%
dplyr::filter(node == 2833)
ttree=tax_table(ttree)
ttree=as.data.frame(ttree) %>% tibble()
ttree=ttree %>% dplyr::filter(Phylum == "Bacteroidetes")
ttree %>% select(Genus, Species) %>%
dplyr::rename(parent="Genus", node="Species") %>%
mutate(branch.length=1, label=node) %>%
tidytree::as.treedata()
ttree %>% pivot_longer(c(Species, Genus, Family, Order, Class, Phylum, Kingdom), names_to = "level", values_to = "name")
```
```{r}
sam %>%
select(ID, npt83, npt84, npt85, npt2002, npt17) %>% pivot_longer(2:6, names_to = "year", values_to = "npt") %>%
filter(!is.na(npt)) %>% mutate(year=dplyr::recode(year, `npt83`=1983, `npt84`=1984, `npt85`=1985, `npt2002`=2002, `npt17`=2017)) %>%
ggplot(aes(year, npt, group=ID, color=ID)) + geom_point() + geom_line() +
theme_minimal() + theme(legend.position = "none")
```
```{r}
fs=bracer::glob("differential_abundance_results/*.ancombc.csv")
res=bind_rows(lapply(bracer::glob("differential_abundance_results/*.ancomb.csv"), function(x) read_csv(x) %>% mutate(comparison=str_split(str_split(x, "/")[[1]][2], "\\.")[[1]][1])))
```
```{r}
rvar=read_csv("var_new_names.csv")
rvar$`Variable name`=str_trim(rvar$`Variable name`)
setdiff(rvar$`Variable name`, colnames(sam))
#"SWL" "MIH_GSI" "DAS"
sam=sam %>%
sam[,rvar$`Variable name`]
```
```{r}
mat=res %>% dplyr::filter(pval < 0.005) %>% pivot_wider(id_cols = c(taxon, comparison), names_from = "comparison", values_from = "stat", values_fill = 0)
mat=as.data.frame(mat)
rownames(mat)=mat[,1]
mat=mat[,-1]
mat=mat %>% as.matrix()
Heatmap(mat, name = "W", show_row_names = F)
```
```{r}
mat=res %>% mutate(taxon=str_c(taxon_rank, taxon, sep = " ")) %>% dplyr::filter(!str_detect(comparison, "scl")) %>%
pivot_wider(id_cols = c(taxon, comparison), names_from = "comparison", values_from = "stat", values_fill = 0)
mat=as.data.frame(mat)
rownames(mat)=mat[,1]
mat=mat[,-1]
mat=mat %>% as.matrix()
Heatmap(mat, name = "W", show_row_names = F)
```
```{r}
fs=bracer::glob("differential_pathway_abundance/*.pathway.csv")
res=bind_rows(lapply(fs, function(x) read_csv(x) %>% mutate(comparison=str_split(str_split(x, "/")[[1]][2], "\\.")[[1]][1])))
```
```{r}
mat=res %>% dplyr::filter(pval < 0.005) %>% pivot_wider(id_cols = c(pathway, comparison), names_from = "comparison", values_from = "stat", values_fill = 0)
mat=as.data.frame(mat)
rownames(mat)=mat[,1]
mat=mat[,-1]
mat=mat %>% as.matrix()
Heatmap(mat, name = "W", show_row_names = F)
```
```{r}
mat=res %>% mutate(taxon=str_c(taxon_rank, taxon, sep = " ")) %>% dplyr::filter(!str_detect(comparison, "scl")) %>%
pivot_wider(id_cols = c(taxon, comparison), names_from = "comparison", values_from = "stat")
mat=as.data.frame(mat)
rownames(mat)=mat[,1]
mat=mat[,-1]
mat=mat %>% as.matrix()
Heatmap(mat, name = "W", show_row_names = F)
```
```{r}
gltree=tax_glom(ds, "Species")
```
```{r}
r=res[(res$qval < 0.05) & (res$taxon_rank == "Species"),]
plot_tree(prune_taxa(r$taxon, tax_glom(ds, "Species")))
phy_tree((prune_taxa(r$taxon, aggregate_taxa(ds, "Species"))))
plot_tree(gltree, )
```
```{r}
dds=prune_samples(sam[sam$extreme != "non_extreme",]$ID, ds)
plot_bar(prune_taxa(r$taxon, aggregate_taxa(dds, "Species")), fill="Species", facet_grid = ~ extreme)
```
# Ordination
```{r}
distanceMethodList
```
```{r}
df=tibble()
for(d in c(distanceMethodList$vegdist, "jsd"))
{
print(d)
m=ds %>% ordinate("MDS", d)
m=m$vectors %>% as.data.frame(row.names = F) %>% mutate(ID=rownames(m$vectors), distance=d) %>% tibble()
df=bind_rows(df, m[,c("ID", "Axis.1", "Axis.2", "Axis.3", "distance")])
}
```
```{r}
param="extreme"
library(ggthemes)
df %>% dplyr::select(ID, `Axis.1`, `Axis.2`, `distance`) %>% left_join(sam, by="ID") %>%
group_by(distance) %>% mutate_at(vars("Axis.1", "Axis.2"), scale) %>% ungroup() %>%
dplyr::filter(!(distance %in% c("raup", "altGower", "gower", "euclidean"))) %>%
dplyr::filter(!is.na(!!rlang::sym(param))) %>%
ggplot(aes(Axis.1, Axis.2, color=!!rlang::sym(param), fill=!!rlang::sym(param))) + geom_point() +
stat_ellipse() + theme_minimal() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(),
axis.text = element_blank(), axis.ticks = element_blank()) +
facet_wrap(~distance, scales="free")
```
```{r}
r=sam %>% as.data.frame()
rownames(r)=r$ID
r$Sample=r$ID
r=r[,colnames(r) != "ID"]