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02_ist_cst_results.Rmd
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02_ist_cst_results.Rmd
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---
title: "IST/CST Results"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
refined = 'intermediates'
private = 'private'
figure = 'working_figure'
```
```{r}
#Figure 3 IST Composition Heatmaps
library("readr")
library("tidyverse")
library("RColorBrewer")
library("pheatmap")
library("viridis")
library("ggplot2")
library("igraph")
library("slam")
library("scales")
#Name of IST/cluster column in metadata
tphe.cc <- "TPHE.IST"
ics.cc <- "ICS.IST"
#Read in sample metadata
pth = file.path(refined, 'dmn')
tphe.md <- read_delim(file.path(pth, "tphe_md.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
ics.md <- read_delim(file.path(pth, "ics_md.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
rns <- tphe.md[[1]]
tphe.md <- tphe.md[ , 2:ncol(tphe.md)]
rownames(tphe.md) <- rns
rns <- ics.md[[1]]
ics.md <- ics.md[ , 2:ncol(ics.md)]
rownames(ics.md) <- rns
#Read in sample composition tables
tphe.cd4 <- read_delim(file.path(pth, "tphe_cd4_comp.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
tphe.cd8 <- read_delim(file.path(pth, "tphe_cd8_comp.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
ics.cd4 <- read_delim(file.path(pth, "ics_cd4_comp.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
ics.cd8 <- read_delim(file.path(pth, "ics_cd8_comp.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
rns <- tphe.cd4[[1]]
tphe.cd4 <- tphe.cd4[ , 2:ncol(tphe.cd4)]
rownames(tphe.cd4) <- rns
rns <- tphe.cd8[[1]]
tphe.cd8 <- tphe.cd8[ , 2:ncol(tphe.cd8)]
rownames(tphe.cd8) <- rns
rns <- ics.cd4[[1]]
ics.cd4 <- ics.cd4[ , 2:ncol(ics.cd4)]
rownames(ics.cd4) <- rns
rns <- ics.cd8[[1]]
ics.cd8 <- ics.cd8[ , 2:ncol(ics.cd8)]
rownames(ics.cd8) <- rns
#Match metadata and composition data
tphe.md <- data.frame(tphe.md)
ics.md <- data.frame(ics.md)
tphe.cd4 <- data.matrix(tphe.cd4)
tphe.cd8 <- data.matrix(tphe.cd8)
ics.cd4 <- data.matrix(ics.cd4)
ics.cd8 <- data.matrix(ics.cd8)
tphe.cd4 <- tphe.cd4[ , (colnames(tphe.cd4) %in% rownames(tphe.md))]
tphe.cd8 <- tphe.cd8[ , (colnames(tphe.cd8) %in% rownames(tphe.md))]
ics.cd4 <- ics.cd4[ , (colnames(ics.cd4) %in% rownames(ics.md))]
ics.cd8 <- ics.cd8[ , (colnames(ics.cd8) %in% rownames(ics.md))]
#Construct properly formatted annotations, color scheme, and composition matrices for use in pheatmap
tphe.cd4.anno <- tphe.md[colnames(tphe.cd4), tphe.cc, drop=FALSE]
tphe.cd8.anno <- tphe.md[colnames(tphe.cd8), tphe.cc, drop=FALSE]
ics.cd4.anno <- ics.md[colnames(ics.cd4), ics.cc, drop=FALSE]
ics.cd8.anno <- ics.md[colnames(ics.cd8), ics.cc, drop=FALSE]
tphe.cd4.anno[[tphe.cc]] <- factor(tphe.cd4.anno[[tphe.cc]])
tphe.cd8.anno[[tphe.cc]] <- factor(tphe.cd8.anno[[tphe.cc]])
ics.cd4.anno[[ics.cc]] <- factor(ics.cd4.anno[[ics.cc]])
ics.cd8.anno[[ics.cc]] <- factor(ics.cd8.anno[[ics.cc]])
tphe.cd4.anno <- tphe.cd4.anno[order(tphe.cd4.anno[[tphe.cc]]), , drop=FALSE]
tphe.cd8.anno <- tphe.cd8.anno[order(tphe.cd8.anno[[tphe.cc]]), , drop=FALSE]
ics.cd4.anno <- ics.cd4.anno[order(ics.cd4.anno[[ics.cc]]), , drop=FALSE]
ics.cd8.anno <- ics.cd8.anno[order(ics.cd8.anno[[ics.cc]]), , drop=FALSE]
colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n=7, name="RdYlBu")), bias=3)(100)
tphe.cd4.mat <- tphe.cd4[, rownames(tphe.cd4.anno), drop=FALSE]
tphe.cd8.mat <- tphe.cd8[, rownames(tphe.cd8.anno), drop=FALSE]
ics.cd4.mat <- ics.cd4[, rownames(ics.cd4.anno), drop=FALSE]
ics.cd8.mat <- ics.cd8[, rownames(ics.cd8.anno), drop=FALSE]
tphe.cd4.mat <- t(apply(tphe.cd4.mat, 1L, scales::rescale))
tphe.cd8.mat <- t(apply(tphe.cd8.mat, 1L, scales::rescale))
ics.cd4.mat <- t(apply(ics.cd4.mat, 1L, scales::rescale))
ics.cd8.mat <- t(apply(ics.cd8.mat, 1L, scales::rescale))
#Make the heatmaps
pheatmap(mat = tphe.cd4.mat, color = colors, annotation_col = tphe.cd4.anno, cluster_rows = FALSE, cluster_cols = FALSE, show_colnames = FALSE, gaps_col = cumsum(unname(table(tphe.cd4.anno[[tphe.cc]]))))
pheatmap(mat = ics.cd4.mat, color = colors, annotation_col = ics.cd4.anno, cluster_rows = FALSE, cluster_cols = FALSE, show_colnames = FALSE, gaps_col = cumsum(unname(table(ics.cd4.anno[[ics.cc]]))))
pheatmap(mat = tphe.cd8.mat, color = colors, annotation_col = tphe.cd8.anno, cluster_rows = FALSE, cluster_cols = FALSE, show_colnames = FALSE, gaps_col = cumsum(unname(table(tphe.cd8.anno[[tphe.cc]]))))
pheatmap(mat = ics.cd8.mat, color = colors, annotation_col = ics.cd8.anno, cluster_rows = FALSE, cluster_cols = FALSE, show_colnames = FALSE, gaps_col = cumsum(unname(table(ics.cd8.anno[[ics.cc]]))))
```
```{r}
#Figure 3 IST Occurence Over PMA
library(readr)
library(ggplot2)
library(tidyverse)
library(ggbeeswarm)
# To get CGA at timepoints
timeline = read_csv(file.path('data', 'subject_timeline.csv')) %>% mutate(SampleID = str_c(Subject, '_', `Sequence Num`))
subject = read_csv(file.path('data', 'subject_covariates.csv')) %>% mutate(GAB = 37 - preterm_weeks, BirthCohort = ifelse(preterm_weeks > 0, 'Pre-term', 'Full-term'))
timeline = left_join(timeline, subject)
# recode by mean CGA
recode_ist = function(tab){
s = tab %>% group_by(IST) %>% summarize(mcga = mean(CGA)) %>% arrange(mcga)
s = s %>% mutate(oIST = fct_reorder(factor(str_c('oIST_', seq_len(nrow(.)))), mcga), oIST_num = as.numeric(oIST))
left_join(tab, s)
}
# ICS by Subject and timepoint
pth = file.path(refined, 'dmn')
tphe <- read_delim(file.path(pth, "tphe_md.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600) %>%
mutate(IST = gsub('TPHE IST ([0-9])', 'TPHE_\\1', `TPHE IST`)) %>% left_join(timeline)%>% select(Subject, CGA = cga, IST, GAB, Visit = `Sequence Num`, BirthCohort) %>% recode_ist
ics <- read_delim(file.path(pth, "ics_md.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)%>%
mutate(IST = gsub('ICS IST ([0-9])', 'ICS_\\1', `ICS IST`)) %>% left_join(timeline) %>% select(Subject, CGA = cga, IST, GAB,Visit = `Sequence Num`, BirthCohort) %>% recode_ist
# ics <- read_tsv(file.path(pth, "ics_basic.txt")) %>% recode_ist()
# tphe <- read_tsv(file.path(pth, "tphe_basic.txt")) %>% recode_ist()
ggplot(ics, aes(y=CGA, x=oIST, color=GAB )) + scale_color_gradient2(midpoint=37, low="red", mid="blue", high="darkblue", space ="Lab" ) + geom_quasirandom() + coord_flip()
```
These were reordered by average PMA in the version of this figure in the paper.
```{r}
summary(lm(CGA ~ IST, data = ics))
```
```{r}
ggplot(tphe, aes(y=CGA, x=oIST, color=GAB )) + scale_color_gradient2(midpoint=37, low="red", mid="blue", high="darkblue", space ="Lab" ) + geom_quasirandom() + coord_flip()
```
```{r}
summary(lm(CGA ~ IST, data = tphe))
```
## Co-occurance of t cell measures
```{r}
ics_tphe = bind_rows(ICS = ics, TPHE = tphe, .id = 'assay')
assays_avail = ics_tphe %>% group_by(Subject, Visit, BirthCohort) %>% summarize(assays = str_c(assay, collapse = '_')) %>% mutate(assays = factor(assays, levels = c('ICS', 'TPHE', 'ICS_TPHE')))
counts_by_subj = assays_avail %>% group_by(Subject, assays, BirthCohort) %>% summarize(n = n()) %>% ungroup() %>% arrange(Subject, desc(n))
# Take modal scenario if a subject had different assays available at different time points (uncommon)
counts_by_subj = counts_by_subj[!duplicated(counts_by_subj$Subject),]
assays_by_term = with(counts_by_subj, table(n, BirthCohort, assays))
ftab = ftable(assays_by_term, row.vars = c('n', 'BirthCohort'))
ftab
write.ftable(ftab, file.path(refined, 'assay_consort_alternative.txt'))
```
Number of subjects with 1, 2 or 3 samples of the various assays, stratified by Term.
```{r t_ist_traj, dev = c('png', 'pdf'), fig.width = 2, fig.height = 4}
ics_tphe = ics_tphe %>% mutate(Subjectf = fct_reorder(factor(Subject), GAB))
traj_plot = ggplot(ics_tphe, aes(y = Subjectf, x = CGA, fill = oIST_num)) +
geom_point(pch = 22) + scale_fill_distiller('IST', palette = 'GnBu') + facet_wrap(~assay) +
theme_minimal() + scale_y_discrete(breaks = NULL) + ylab("Subjects") + xlab('PMA') + geom_text(aes(label = oIST_num), size = 1.5) + theme(legend.position = 'bottom')
trajs = ics_tphe %>% group_by(assay) %>% do(plot = {
out = traj_plot %+% .
print(out)
out
})
```
```{r}
#Figure 4 CST Composition Heatmap
library("readr")
library("tidyverse")
library("RColorBrewer")
library("pheatmap")
library("viridis")
library("ggplot2")
library("igraph")
library("slam")
library("scales")
CLUSTER_COLUMN <- "Renamed_CST"
md.rec <- read_delim(file.path(pth, "rec_basic.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
rns <- md.rec[[1]]
md.rec <- md.rec[ , 2:ncol(md.rec)]
rownames(md.rec) <- rns
md.nas <- read_delim(file.path(pth, "nas_basic.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
rns <- md.nas[[1]]
md.nas <- md.nas[ , 2:ncol(md.nas)]
rownames(md.nas) <- rns
genera.rec <- read_delim(file.path(refined, "REC_top_taxa.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
rns <- genera.rec[[1]]
genera.rec <- genera.rec[ , 2:ncol(genera.rec)]
rownames(genera.rec) <- rns
genera.nas <- read_delim(file.path(refined, "NAS_top_taxa.txt"), "\t", escape_double = FALSE, trim_ws = TRUE, guess_max = 3600)
rns <- genera.nas[[1]]
genera.nas <- genera.nas[ , 2:ncol(genera.nas)]
rownames(genera.nas) <- rns
md.rec <- data.frame(md.rec)
md.nas <- data.frame(md.nas)
genera.rec <- data.matrix(genera.rec)
genera.nas <- data.matrix(genera.nas)
genera.rec <- genera.rec[ , (colnames(genera.rec) %in% rownames(md.rec))]
genera.nas <- genera.nas[ , (colnames(genera.nas) %in% rownames(md.nas))]
anno.rec <- md.rec[colnames(genera.rec), CLUSTER_COLUMN, drop=FALSE]
anno.nas <- md.nas[colnames(genera.nas), CLUSTER_COLUMN, drop=FALSE]
cga.rec <- md.rec[colnames(genera.rec), c(CLUSTER_COLUMN, "CGA"), drop=FALSE]
cga.nas <- md.nas[colnames(genera.nas), c(CLUSTER_COLUMN, "CGA"), drop=FALSE]
anno.rec[[CLUSTER_COLUMN]] <- fct_reorder(cga.rec[[CLUSTER_COLUMN]], cga.rec$CGA, mean)
anno.nas[[CLUSTER_COLUMN]] <- fct_reorder(cga.nas[[CLUSTER_COLUMN]], cga.nas$CGA, mean)
anno.rec <- anno.rec[order(anno.rec[[CLUSTER_COLUMN]]), , drop=FALSE]
anno.nas <- anno.nas[order(anno.nas[[CLUSTER_COLUMN]]), , drop=FALSE]
top25.rec <- head(names(rev(sort(rowSums(genera.rec)))), 25)
top25.nas <- head(names(rev(sort(rowSums(genera.nas)))), 25)
colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n=7, name="RdYlBu")), bias=3)(100)
mat.rec <- genera.rec[top25.rec, rownames(anno.rec), drop=FALSE]
mat.nas <- genera.nas[top25.nas, rownames(anno.nas), drop=FALSE]
mat.rec <- t(apply(mat.rec, 1L, scales::rescale))
mat.nas <- t(apply(mat.nas, 1L, scales::rescale))
pheatmap(mat = mat.rec, color = colors, annotation_col = anno.rec, cluster_rows = TRUE, cluster_cols = FALSE, show_colnames = FALSE, gaps_col = cumsum(unname(table(anno.rec[[CLUSTER_COLUMN]]))))
pheatmap(mat = mat.nas, color = colors, annotation_col = anno.nas, cluster_rows = TRUE, cluster_cols = FALSE, show_colnames = FALSE, gaps_col = cumsum(unname(table(anno.nas[[CLUSTER_COLUMN]]))))
```
```{r}
#Figure 4 CST Occurence Over PMA
library(readr)
library(ggplot2)
library(tidyverse)
library(ggbeeswarm)
recode_cst = function(tab){
s = tab %>% group_by(CST) %>% summarize(mcga = mean(CGA)) %>% arrange(mcga)
s = s %>% mutate(oCST = fct_reorder(factor(str_c('oCST_', seq_len(nrow(.)))), mcga), oCST_num = as.numeric(oCST))
left_join(tab, s)
}
rec <- read_tsv(file.path(pth, "rec_basic.txt")) %>% recode_cst
nas <- read_tsv(file.path(pth, "nas_basic.txt")) %>% recode_cst
ggplot(rec, aes(y=CGA, x=Renamed_CST, color=gaBirth)) + scale_color_gradient2(midpoint=37, low="red", mid="blue", high="darkblue", space ="Lab" ) + geom_quasirandom() + coord_flip()
ggplot(nas, aes(y=CGA, x=Renamed_CST, color=gaBirth)) + scale_color_gradient2(midpoint=37, low="red", mid="blue", high="darkblue", space ="Lab" ) + geom_quasirandom() + coord_flip()
```
```{r}
communities = bind_rows(
list(ics = ics, tphe = tphe), .id = 'community'
) %>% rename(Renamed_CST = IST) %>%
bind_rows(bind_rows(list(nas = nas, rec = rec), .id = 'community')) %>%
mutate(Subject = factor(Subject), Renamed_CST = factor(Renamed_CST))
hospital_humilk = read_csv('data/milk_hospital.csv') %>% rename(perinatal_milk = `Any Human Milk Perinatal`)
nabx_polish = read_csv('data/antibiotic_exposure.csv')
covariates = read_csv("data/subject_covariates.csv")
timeline = read_csv("data/subject_timeline.csv") %>% filter()
nabx_time = nabx_polish %>% select(-group) %>% rename(n_antibiotics = `Number of systemic antibiotic`) %>% spread(discharge, n_antibiotics) %>% rename(n_antibiotics_discharge = 'TRUE', n_antibiotics_pre = 'FALSE')
discharge_humilk = read_csv( 'intermediates/milk_subject.csv')
covariates = purrr::reduce(list(covariates, hospital_humilk, nabx_time, discharge_humilk), left_join)
covariates$n_antibiotics_pre = ifelse(covariates$preterm_weeks <=0, NA, covariates$n_antibiotics_pre)
has_any_cst = communities %>%
group_by(Subject, Renamed_CST, .drop = FALSE) %>%
summarize(has_cst = n()>0) %>% ungroup()
was_sampled = communities %>% group_by(community, Subject, .drop = FALSE) %>%
summarize(was_sampled = n() > 0) %>%
filter(was_sampled) %>%
ungroup() %>%
inner_join(communities %>% group_by(community, Renamed_CST) %>% summarize())
has_cst = communities %>% left_join(was_sampled, by = c('community', 'Subject'), suffix = c('', '_test')) %>%
group_by(Subject, Renamed_CST_test, .drop = FALSE) %>%
mutate(is_Renamed_CST = Renamed_CST_test == Renamed_CST) %>% ungroup()
nrow(has_any_cst)
has_any_cst = has_any_cst %>% semi_join(was_sampled)
nrow(has_any_cst)
has_cst = has_cst %>% left_join(covariates, by = 'Subject') %>%
mutate(mode_delivery = fct_recode(mode_delivery, vaginal = c('Vaginal Vertex'), other = 'Vaginal Breech', other= 'Caesarean Section'),
n_antibiotics_pre = ifelse(preterm_weeks<=0, 0, n_antibiotics_pre))%>% mutate_at(.vars = vars(CGA, milk_months, n_antibiotics_pre, n_antibiotics_discharge), .fun = scale)
```
```{r mm-cst-assoc}
cst_assoc = has_cst %>% group_by(Renamed_CST_test) %>% do({
data = .
full = lme4::glmer(is_Renamed_CST ~ preterm_weeks + mode_delivery + preg_antibiotics + milk_months + perinatal_milk + n_antibiotics_pre + n_antibiotics_discharge + (1|Subject), family = 'binomial', data = data, nAGQ = 0L)
tibble(full = list(full))
})
cst_assoc = pivot_longer(cst_assoc, -Renamed_CST_test, values_to = 'model') %>%
rowwise() %>% mutate(result = list(suppressWarnings(broom::tidy(model))))
cst_coef = unnest(cst_assoc %>% select(-model), cols = c(result))
knitr::kable(cst_coef %>% filter(term == "preterm_weeks") %>% arrange(p.value)) %>% head(n=20)
write_csv(cst_coef, 'intermediates/cst_assoc.csv')
```
Mixed model adjusting for `r as.character(formula(cst_assoc$model[[1]]))[3]`
Top 20 associations listed above, others [are here](intermediates/cst_assoc.csv).
# Trajectories
```{r cst_traj, dev = c('png', 'pdf'), fig.width = 4, fig.height = 6}
rec_nas = bind_rows(REC = rec, NAS = nas, .id = 'assay') %>% rename(GAB = gaBirth)
rec_nas = rec_nas %>% mutate(Subjectf = fct_reorder(factor(Subject), GAB))
traj_plot = ggplot(rec_nas, aes(y = Subjectf, x = CGA, fill = oCST_num)) +
geom_point(pch = 22) + scale_fill_distiller('CST', palette = 'GnBu') + facet_wrap(~assay) +
theme_minimal() + scale_y_discrete(breaks = NULL) + ylab("Subjects") + xlab('PMA') + geom_text(aes(label = oCST_num), size = 1.5) +
theme(legend.position = 'bottom')
trajs2 = rec_nas %>% group_by(assay) %>% do(plot = {
out = traj_plot %+% .
print(out)
out
})
```
```{r combined_traj, dev = c('png', 'pdf'), fig.width=8, fig.height=6}
cowplot::plot_grid(plotlist = trajs$plot, ncol = 2)
cowplot::plot_grid(plotlist = trajs2$plot, ncol = 2)
```
#Supplementary Figure 3 PCoA Plots
Runs in qiime.
```sh
qiime diversity core-metrics-phylogenetic --i-phylogeny nas_rooted_tree.qza --i-table nas_table.qza --p-sampling-depth 1200 --m-metadata-file nas_basic.txt --output-dir nas_cda --p-n-jobs 24
qiime diversity core-metrics-phylogenetic --i-phylogeny rec_rooted_tree.qza --i-table rec_table.qza --p-sampling-depth 2250 --m-metadata-file rec_basic.txt --output-dir rec_cda --p-n-jobs 24
```
```{r}
#Supplementary Figure 3 Axis Density by Term
library(readr)
library(ggplot2)
rec <- read_tsv(file.path(pth, "rec_basic.txt"))
rec$BirthCohort <- ifelse(rec$gaBirth >= 37, "Full term", "Preterm")
pc1 <- read_tsv(file.path('intermediates', "rec_pc1.txt"))
df <- merge(rec, pc1, by = "SampleID")
ggplot(data = df, aes(x = PC1, group = BirthCohort, fill = BirthCohort)) + geom_density(adjust=1.5, position="fill")
nas <- read_tsv(file.path(pth, "nas_basic.txt"))
nas$BirthCohort <- ifelse(nas$gaBirth >= 37, "Full term", "Preterm")
pc1 <- read_tsv(file.path('intermediates', "nas_pc1.txt"))
df <- merge(nas, pc1, by = "SampleID")
ggplot(data = df, aes(x = PC1, group = BirthCohort, fill = BirthCohort)) + geom_density(adjust=1.5, position="fill")
```