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demo_dreamlet.Rmd
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---
title: "Illustration of dreamlet on subset of NPS/AD"
subtitle: 'STAR-solo R1-4 merged h5ad'
author: "Developed by [Gabriel Hoffman](http://gabrielhoffman.github.io/)"
date: "Run on `r Sys.time()`"
documentclass: article
output:
html_document:
toc: true
smart: false
vignette: >
%\VignetteIndexEntry{Decorrelate}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
%\usepackage[utf8]{inputenc}
---
<!---
# results
# /sc/arion/projects/CommonMind/hoffman/NPS-AD/work/nps_ad/demo/results
cd /sc/arion/projects/CommonMind/hoffman/NPS-AD/work/nps_ad/
ml python git
git pull
R
system("git pull"); rmarkdown::render("demo_dreamlet.Rmd");
# https://hoffmg01.u.hpc.mssm.edu/nps_ad/demo_dreamlet.html
ml git
cd ~/build2/dreamlet
git pull
R CMD INSTALL .
--->
* Analyses to implement
+ Concordance as a function of
+ number donors
+ number of cells
+ mixed model
+ batch effect
+ which genes have batch effect?
+ compare cell tpyes Demonstrate performance by sampling two populations from the same cell cluster, and running showing the unpaired doesnt control false positive rate. How to demonstrate false positive rate.
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE,
warning=FALSE,
message=FALSE,
error = FALSE,
tidy = FALSE,
dev = c("png", "pdf"),
cache = TRUE,
cache.lazy = FALSE)
```
```{r load.packages, cache=FALSE}
# Use cache=FALSE so that package are fully loaded each time
# This ensures that forks within mclapply() have these loaded
# Othewise, mclapply() not have access to these libraries and will fail
# unless the libraries are manually loaded within each fork
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(zellkonverter)
library(DelayedArray)
library(HDF5Array)
library(GSEABase)
library(dreamlet)
library(muscat)
library(cowplot)
library(zenith)
library(scater)
library(data.table)
library(S4Vectors)
library(tidyverse)
library(kableExtra)
library(qvalue)
library(corrplot)
library(org.Hs.eg.db)
library(broom)
})
get_pi1 = function(p){
res <- tryCatch( pi0est(p), error = function(e) NULL)
pi1 = NA
if( ! is.null(res)){
pi1 = 1 - res$pi0
}
pi1
}
```
```{r load.data, cache=FALSE}
outfolder = '/sc/arion/projects/CommonMind/hoffman/NPS-AD/work/nps_ad/demo/results/'
datafile = paste(outfolder, "demo_dreamlet_sce.RDS")
if( file.exists(datafile) ){
# reading data from RDS is much faster:
# especially good for pipeline development
sceCombine = readRDS( datafile )
}else{
# read snRNA-seq data with annotations build in
h5ad_file = "/sc/arion/projects/CommonMind/leed62/NPS-AD/analysis_solo/NPS-AD_roundMerged_pass2_anno.h5ad"
# read raw/* from h5ad file
sce_in = readH5AD(h5ad_file, use_hdf5=TRUE, raw=TRUE, verbose=TRUE)
# extract raw counts
sceCombine = altExp( sce_in, "raw", withDimnames=TRUE, withColData=TRUE)
sceCombine = as(sceCombine, "SingleCellExperiment")
reducedDims(sceCombine) <- reducedDims(sce_in)
# set metadata to empty to reduce size and speep up development time
metadata(sceCombine) = list()
# collapse GABAergic neurons
sceCombine$celltype8 = sceCombine$celltype %>%
as.character %>%
recode(
"GABAergic SST interneuron" = 'GABAergic neuron',
"GABAergic VIP interneuron" = 'GABAergic neuron',
"GABAergic PVALB interneuron" = 'GABAergic neuron') %>%
as.factor
saveRDS(sceCombine, file=datafile)
}
```
Data freeze includes `r length(table(sceCombine$Channel))` samples, `r length(table(sceCombine$round_num))` rounds, `r length(table(sceCombine$batch))` 10X batches, `r length(table(sceCombine$SubID))` donors, and `r format(ncol(sceCombine), big.mark=',')` cells passing QC.
# Joint UMAP
```{r umap2, dev="png"}
# extract UMAP coordinates and annotations
df = cbind(reducedDim(sceCombine, "X_umap"), colData(sceCombine)[,c("anno", "celltype", 'celltype8', "class", "subtype", "leiden_labels")])
df = data.frame(df)
ggplot(df, aes(V1, V2, color=celltype)) + geom_point(size=.05) + theme_classic() + theme(aspect.ratio=1) + guides(colour = guide_legend(override.aes = list(size = 1.5))) + xlab("UMAP1") + ylab("UMAP2")
```
```{r umap3, dev="png"}
ggplot(df, aes(V1, V2, color=class)) + geom_point(size=.05) + theme_classic() + theme(aspect.ratio=1) + guides(colour = guide_legend(override.aes = list(size = 1.5))) + xlab("UMAP1") + ylab("UMAP2")
```
```{r umap8, dev="png"}
ggplot(df, aes(V1, V2, color=celltype8)) + geom_point(size=.05) + theme_classic() + theme(aspect.ratio=1) + guides(colour = guide_legend(override.aes = list(size = 1.5))) + xlab("UMAP1") + ylab("UMAP2")
```
```{r combineData}
# Name batches by samples in them
df_batch_info = colData(sceCombine) %>%
as_tibble %>%
group_by(batch) %>%
summarize(batchID = paste(sort(unique(SubID)), collapse=',')) %>%
distinct
colData(sceCombine)$BatchID = as.character(colData(sceCombine)$batch)
colData(sceCombine)$BatchID = sapply(colData(sceCombine)$BatchID, function(x){
with(df_batch_info, batchID[batch==x])
})
names(colData(sceCombine)$BatchID) = c()
colData(sceCombine)$BatchID = as.factor(colData(sceCombine)$BatchID)
# Specify how to collapse into pseudo-bulk
sceCombine$id <- paste0(sceCombine$batch, '_', sceCombine$SubID)
colData(sceCombine)$splitAB = ifelse( colData(sceCombine)$BatchID %in% levels(colData(sceCombine)$BatchID)[1:45], "A", "B")
datafile = paste(outfolder, "demo_dreamlet_sce_mod.RDS")
saveRDS(sceCombine, file=datafile)
# Create pseudo-bulk SingleCellExperiment
pbObj <- aggregateToPseudoBulk(sceCombine,
assay = "X",
fun = "sum",
cluster_id = "celltype8",
sample_id = "id")
# Combine with phenotype data at the Donor level
# read metadata
meta_file = "/sc/arion/projects/CommonMind/leed62/NPS-AD/MSSM_HBCC_RUSH_clinical_metadata_combo.csv"
df_meta = fread(meta_file)
df_meta = df_meta[,-1]
# save original order of samples
colData(pbObj)$row.order = 1:nrow(colData(pbObj))
# g = intersect(colnames(colData(pbObj)), colnames(df_meta))
# merge colData with additional metadata
df_merge = merge(colData(pbObj), df_meta, by="SubID", all.x=TRUE)
# set rownames to be same as original, based on row.order
# These rownames determine colnames(pbObj), which is used downstream
rownames(df_merge) = rownames(colData(pbObj))[df_merge$row.order]
# extract batch info per sample
# df_batching = with( colData(sceCombine), unique(data.frame(id, round_num, batch, prep, HTO)))
# df_merge = merge(df_merge, df_batching, by.x= "row.names", by.y="id", all.x=TRUE)
# rownames(df_merge) = df_merge$Row.names
# df_merge = df_merge[,-1]
# save merged data as colData while retaining original cell order
colData(pbObj) = df_merge[order(df_merge$row.order),]
colData(pbObj)$row.order = c()# remove row.order
# Set levels for Dx2
lvls = sort(unique(as.character(colData(pbObj)$Dx2)))
lvls = lvls[-which(lvls=="Control")]
colData(pbObj)$Dx2 = factor(colData(pbObj)$Dx2, c("Control", lvls))
saveRDS(pbObj, file="/sc/arion/projects/CommonMind/hoffman/NPS-AD/work/demo_dreamlet_pbobj.RDS" )
```
### Pseudobulk at Donor level
```{r combineData.donor}
# Create pseudo-bulk SingleCellExperiment
pbObj.Donor <- aggregateToPseudoBulk(sceCombine,
assay = "X",
fun = "sum",
cluster_id = "celltype8",
sample_id = "SubID")
# Combine with phenotype data at the Donor level
# read metadata
meta_file = "/sc/arion/projects/CommonMind/leed62/NPS-AD/MSSM_HBCC_RUSH_clinical_metadata_combo.csv"
df_meta = fread(meta_file)
df_meta = df_meta[,-1]
# save original order of samples
colData(pbObj.Donor)$row.order = 1:nrow(colData(pbObj.Donor))
# g = intersect(colnames(colData(pbObj)), colnames(df_meta))
# merge colData with additional metadata
df_merge = merge(colData(pbObj.Donor), df_meta, by.x="row.names", by.y="SubID", all.x=TRUE)
# set rownames to be same as original, based on row.order
# These rownames determine colnames(pbObj.Donor), which is used downstream
rownames(df_merge) = rownames(colData(pbObj.Donor))[df_merge$row.order]
# extract batch info per sample
# df_batching = with( colData(sceCombine), unique(data.frame(id, round_num, batch, prep, HTO)))
# df_merge = merge(df_merge, df_batching, by.x= "row.names", by.y="id", all.x=TRUE)
# rownames(df_merge) = df_merge$Row.names
# df_merge = df_merge[,-1]
# save merged data as colData while retaining original cell order
colData(pbObj.Donor) = df_merge[order(df_merge$row.order),]
colData(pbObj.Donor)$row.order = c()# remove row.order
# Set levels for Dx2
lvls = sort(unique(as.character(colData(pbObj.Donor)$Dx2)))
lvls = lvls[-which(lvls=="Control")]
colData(pbObj.Donor)$Dx2 = factor(colData(pbObj.Donor)$Dx2, c("Control", lvls))
saveRDS(pbObj.Donor, file="/sc/arion/projects/CommonMind/hoffman/NPS-AD/work/demo_dreamlet_pbobj_Donor.RDS" )
```
# Analysis of joint dataset
```{r round_num.summary}
table(pbObj$round_num)
```
```{r Dx.summary}
df = unique(data.frame(Dx2 = pbObj$Dx2, Donor = pbObj$SubID))
sort(table(df$Dx2), decreasing=TRUE) %>% kbl() %>% kable_styling(full_width=FALSE)
```
```{r voom}
# Normalize and apply voom
res.proc = processAssays( pbObj, ~ (1|SubID) + (1|batch) + (1|HTO) + (1|cohort),
min.cells = 5,
min.count = 10,
# pmetadata = df_collapse,
# pkeys=c('ID', 'anno'),
BPPARAM = SnowParam(16, progressbar=TRUE))
res.proc
```
```{r voom.plot, fig.height=10, fig.width=10, cache=TRUE}
plotVoom( res.proc )
```
```{r voom.plot_sub, fig.height=4, fig.width=10, cache=TRUE}
plotVoom( res.proc[7:9] )
```
```{r varPart}
form = ~ (1|SubID) + (1|batch) + (1|HTO) + (1|Dx2) + (1|Sex) + (1|cohort) + scale(Age) + scale(PMI)
res.vp = fitVarPart(res.proc, form, BPPARAM = SnowParam(11, progressbar=TRUE))
```
```{r vp.plot, fig.height=15, fig.width=10, cache=TRUE}
colnames(res.vp) = gsub("scale\\.", "", colnames(res.vp))
colnames(res.vp) = gsub("\\.", "", colnames(res.vp))
colnames(res.vp) = gsub("SubID", "Donor", colnames(res.vp))
plotVarPart( sortCols(res.vp), label.angle=45 )
```
```{r vp.plot_sub, fig.height=4, fig.width=10, cache=TRUE, eval=FALSE}
idx = res.vp$assay %in% c('GABAergic neuron', 'Glutamatergic neuron', 'Microglia')
plotVarPart( sortCols(res.vp[idx,]), label.angle=70 )
```
### Examine variancePartition results
```{r vp.examine, fig.height=3, fig.width=7}
CT = "Astrocyte"
i = 1:3
df = sortCols(res.vp)[res.vp$assay == CT,]
rownames(df) = df$gene
i = c(i, which.max(df$Donor), which.max(df$Batch), which.max(df$Residuals))
plotPercentBars( data.frame(df[i,-c(1,2)])) + ggtitle(CT)
```
Examples
```{r vp.examine2, fig.height=6, fig.width=8, cache=TRUE}
figList = lapply( df$gene[i], function(geneName){
df = merge( as.data.frame(t(assay(res.proc, CT)$E[geneName,,drop=FALSE])), colData(res.proc), by="row.names")
plotStratifyBy(df, 'SubID', geneName, colorBy=NULL, main=geneName) + theme(aspect.ratio=1)
})
plot_grid( plotlist=figList, nrow=2 )
```
### variance partitioning and eQTLs
Variance partitioning corresponds to genetic regulation of gene expression. Genes with high variation across Donor have high variation explained by cis SNPs. Genes with high variation explained by batch are inversely related to genetic R2.
```{r vp.eqtl}
synapser::synLogin()
df_eqtl_r2 = as.data.frame(fread(synapser::synGet('syn17083748')$path))
res.vp.augment = res.vp %>%
as_tibble %>%
mutate( Donor.augment = Donor + Dx2 + Age + Sex + PMI)
# merge with R^2 for each gene
df = res.vp.augment %>%
pivot_longer(colnames(res.vp.augment)[-c(1,2)])
df = df %>%
inner_join(df_eqtl_r2, by=c('gene' = 'ensembl_gene_id')) %>%
group_by(assay, name) %>%
group_modify(~ tidy(cor.test(.x$value, .x$CMC_R2, method="spearman"))) %>%
mutate(fdr = p.adjust(p.value, "fdr")) %>%
inner_join( df %>% group_by(assay, name) %>% tally) %>%
mutate(se = sqrt((1-estimate^2)/(n-2)))
zmax = max(abs(df$estimate))
df %>%
ggplot(aes(assay, name, fill=estimate, label=ifelse(fdr < 0.05, " #", ''))) +
geom_tile() + geom_text() + scale_fill_gradient2(low="blue", mid="white", high="red", limits=c(-zmax, zmax)) + theme_classic() + theme(axis.text.x = element_text(angle = 75, vjust = 1, hjust=1))
df %>%
filter(name %in% c("batch", "Donor.augment")) %>%
mutate(name = recode_factor(name, batch = "Batch", Donor.augment="Donor")) %>%
ggplot(aes(assay, estimate, fill=name, label=ifelse(fdr < 0.05, " #", ''))) +
geom_bar(stat="identity") +
geom_errorbar(aes(ymin = estimate - 1.96*se, ymax = estimate + 1.96*se), width=0) +
facet_wrap(~name) +
geom_text(y = .15) +
theme_classic() +
coord_flip() +
ylab("Spearman correlation") +
xlab("Cell type") +
theme(aspect.ratio=1, legend.position="none")
```
## Batch effects and gene sets
```{r DE.batch}
library(zenith)
go.gs = get_GeneOntology(to="ENSEMBL")
coef = "batch"
n_genes_min = 10
geneSets = go.gs
res = lapply( unique(res.vp$assay), function(key){
# subset by assay
df = res.vp[res.vp$assay == key,]
# get variance statistic
statistic = df[,coef]
names(statistic) = df$gene
# convert GeneSetCollection to list
geneSets.lst = recodeToList( geneSets )
# Map from Ensembl genes in geneSets_GO to
# from trimmed Ensembl names from RNA-seq data
index = ids2indices( geneSets.lst, names(statistic))
# filter by size of gene set
index = index[vapply(index, length, FUN.VALUE=numeric(1)) >= n_genes_min]
res = cameraPR( statistic, index, use.ranks=TRUE)
res$PValue = pmin(0.999, res$PValue)
DataFrame( assay = key, Geneset = rownames(res), res)
})
res = do.call(rbind, res)
res$FDR = p.adjust(res$PValue, "fdr")
# table(res$FDR < 0.05)
```
```{r zenith.vp, fig.height=10}
res$coef = "BatchEffect"
plotZenithResults(as.data.frame(res), 4, 2)
```
## Correlation with and between donors
```{r within_btw, fig.height=10, fig.width=10}
source("/sc/arion/projects/CommonMind/hoffman/NPS-AD/work/nps_ad/common_analyses.R")
eval_within_across_donor( res.proc, "SubID" )
```
## dreamlet analysis
```{r dreamlet.fit}
form = ~ Sex + (1|SubID) + (1|batch) + (1|HTO) + (1|cohort) + Dx2 + scale(Age) + scale(PMI)
fit.dl = dreamlet( res.proc, form, BPPARAM=SnowParam(36))
saveRDS( fit.dl, "/sc/arion/projects/CommonMind/hoffman/NPS-AD/work/fit.dl.RDS")
```
```{r de.summary}
coefs = c('Dx2AD', 'Dx2SCZ')
tabLst = lapply( coefs, function(key){
tab = topTable(fit.dl, coef=key, number=Inf)
tab %>%
as_tibble %>%
group_by(assay) %>%
summarise( coef = key,
nGenes = length(adj.P.Val),
nDE = sum(adj.P.Val < 0.05),
pi1 = 1 - qvalue(P.Value)$pi0)
})
tab = do.call(rbind, tabLst)
as.data.frame(tab)
```
```{r de.plots}
ggplot(tab, aes(assay, nGenes, fill=assay)) + geom_bar(stat="identity") + facet_wrap(~coef) + theme_classic() + theme(aspect.ratio=1, legend.position="none", axis.text.x = element_text(angle = 45, vjust=1, hjust=1) )
ggplot(tab, aes(assay, nDE, fill=assay)) + geom_bar(stat="identity") + facet_wrap(~coef) + theme_classic() + theme(aspect.ratio=1, legend.position="none", axis.text.x = element_text(angle = 45, vjust=1, hjust=1) )
ggplot(tab, aes(assay, pi1, fill=assay)) + geom_bar(stat="identity") + facet_wrap(~coef) + theme_classic() + theme(aspect.ratio=1, legend.position="none", axis.text.x = element_text(angle = 45, vjust=1, hjust=1) )
```
# Volcanos
```{r volcanos.1, fig.width=7, fig.height=12}
plotVolcano( fit.dl, coef = coefs[1], nGenes=0 ) + ggtitle(coefs[1])
```
```{r volcanos.2, fig.width=7, fig.height=12}
plotVolcano( fit.dl, coef = coefs[2], nGenes=0 ) + ggtitle(coefs[2])
```
# Cross-disease signature analysis
```{r compare.signatures, fig.width=9, fig.height=9}
# get one data.frame with all differential expression scores
df = lapply( coefs, function(coef){
data.frame( coef=coef, topTable(fit.dl, coef=coef, number=Inf))
})
df = do.call(rbind, df)
# dcast as wide matrix
df2 = reshape2::dcast(df, ID ~ coef + assay , value.var="z.std")
colnames(df2) = gsub("^Dx2", '', colnames(df2))
C = cor(df2[,-1], use='pairwise.complete.obs')
col = colorRampPalette(c("blue3", "white","red3"))(200)
corrplot(C, col=col, tl.col="black")
corrplot(C, order="hclust", col=col, tl.col="black")
```
### heivr
```{r heivr}
library(heivr)
tab = topTable(fit.dl, coef="Dx2AD", number=Inf)
df.logFC = tab %>%
as.data.frame %>%
reshape2::dcast(ID ~ assay, value.var="logFC")
df.var = tab %>%
as.data.frame %>%
mutate(se = (logFC/t)^2) %>%
reshape2::dcast(ID ~ assay, value.var="se")
B = df.logFC[,-1]
V = df.var[,-1]
res.heivr = heivrPairs(B, V)
C = pairsToMatrix(res.heivr, "rho")
# C = cor(B, use='pairwise.complete.obs')
png("~/www/test.png")
plotCorrMatrix(C)
dev.off()
```
# zenith for gene set analysis
```{r zenith}
# Load Gene Ontology database
go.gs = get_GeneOntology()
ngenes = sapply(go.gs, function(gs) length(geneIds(gs)))
go.gs = go.gs[(ngenes > 20) & (ngenes < 2000)]
coefs = c('Dx2AD', 'Dx2SCZ')
res.gsa = lapply( coefs, function(key){
# check inter.gene.cor=NA
zenith_gsa(fit.dl, coefs=key, go.gs, inter.gene.cor=.01)
})
names(res.gsa) = coefs
res.gsa_cat = do.call(rbind, res.gsa)
```
```{r zenith.heatmap.1, fig.width=7, fig.height=12}
fig = plotZenithResults(res.gsa[[1]], 3, 1) + ggtitle(names(res.gsa)[1])
```
```{r zenith.heatmap.2, fig.width=7, fig.height=12}
fig = plotZenithResults(res.gsa[[2]], 3, 1) + ggtitle(names(res.gsa)[3])
```
# Split data
```{r split.data}
pbList = list( A = pbObj[,pbObj$splitAB == "A"],
B = pbObj[,pbObj$splitAB == "B"])
# summarize disease status
df = lapply( names(pbList), function(x){
pb = pbList[[x]]
unique(data.frame(Set =x, Dx2 = pb$Dx2, Donor = pb$SubID))
})
df = do.call(rbind, df)
df %>%
group_by(Set) %>%
count(Dx2, Set, sort = TRUE) %>%
kbl() %>% kable_styling(full_width=FALSE)
```
```{r voom.2}
res.procList = lapply( pbList, function(pb){
idx = which(pb$Dx2 %in% c("AD", "Control", "SCZ"))
# Normalize and apply voom
processAssays(pb[,idx], ~ (1|SubID) + (1|batch) + (1|HTO) + (1|cohort),
min.cells = 5,
min.count = 10,
BPPARAM = SnowParam(16, progressbar=TRUE))
})
```
```{r varPart.2}
form = ~ (1|SubID) + (1|batch) + (1|HTO) + (1|Dx2) + (1|Sex) + (1|cohort) + scale(Age) + scale(PMI)
res.vpList = lapply( res.procList, function(x){
fitVarPart(x, form, BPPARAM = SnowParam(12, progressbar=TRUE))
})
```
### Differential expression in each batch
Compare 1) fixed effects collapsing model, 2) full pwLMM, 3) no batch correction
f(res.dlList) to evaluate concordance. Correlation of Dx signal with GC content
```{r dreamlet.2}
form = ~ (1|SubID) + (1|batch) + (1|HTO) + (1|Sex) + (1|cohort) + scale(Age) + scale(PMI) + Dx2
res.dlList = lapply( res.procList, function(x){
dreamlet( x, form, BPPARAM = SnowParam(12, progressbar=TRUE))
})
```
### Differential expression with collapsed counts
```{r collapse}
pbList.Donor = list( A = pbObj.Donor[,pbObj.Donor$splitAB == 'A'],
B = pbObj.Donor[,pbObj.Donor$splitAB == 'B'])
res.procList.Donor = lapply( pbList.Donor, function(pb){
idx = which(pb$Dx2 %in% c("AD", "Control", "SCZ"))
# Normalize and apply voom
processAssays(pb[,idx], ~ cohort,
min.cells = 5,
min.count = 10,
BPPARAM = SnowParam(16, progressbar=TRUE))
})
form = ~ Sex + cohort + scale(Age) + scale(PMI) + Dx2
res.dlList.Donor = lapply( res.procList.Donor, function(x){
dreamlet( x, form, BPPARAM = SnowParam(12, progressbar=TRUE))
})
```
### Concordance between sets
Correlation between t-statistics
```{r de.summary.2}
library(broom)
coefs = c('Dx2AD', 'Dx2SCZ')
eval_concordance = function(res.dlList){
resTab = lapply( coefs, function(key){
tab = lapply( res.dlList, function(fit){
tab = topTable(fit, coef=key, number=Inf)
tab$key = with(tab, paste(assay, ID, key))
data.frame(coef=key, tab)
})
tab = inner_join(tab[[1]], tab[[2]], by='key')
res1 = tab %>%
as_tibble %>%
group_by(assay.x) %>%
group_modify(~ tidy(cor.test(.x$t.x, .x$t.y, method="p"))) %>%
left_join( tab %>% group_by(assay.x) %>% tally) %>%
mutate(coef = key, se = sqrt((1-estimate^2)/(n-2)))
res2 = tab %>%
as_tibble %>%
group_by(assay.x) %>%
summarize( nGenes.x = length(adj.P.Val.x),
nDE.x = sum(adj.P.Val.x < 0.05),
pi1.x = get_pi1(P.Value.x),
nGenes.y = length(adj.P.Val.y),
nDE.y = sum(adj.P.Val.y < 0.05),
pi1.y = get_pi1(P.Value.y),
pi1.rep.x = get_pi1(P.Value.x[adj.P.Val.y <= 0.05]),
pi1.rep.y = get_pi1(P.Value.y[adj.P.Val.x <= 0.05])
) %>%
mutate(key = key, coef = coef)
left_join(res1, res2, by='assay.x')
})
do.call(rbind, resTab)
}
resTab.fixed = eval_concordance(res.dlList.Donor)
resTab.fixed$method = "fixed"
resTab.mixed = eval_concordance(res.dlList)
resTab.mixed$method = "mixed"
resTab.mixed %>%
as_tibble %>%
mutate( fdr = p.adjust(p.value, "fdr")) %>%
ggplot(aes(assay.x, estimate, fill=assay.x, label=ifelse(fdr < 0.05,' #',''))) +
geom_bar(stat="identity") +
theme_classic() +
theme(aspect.ratio=1, legend.position="none", plot.title = element_text(hjust = 0.5)) +
facet_wrap(~coef.x) +
coord_flip() +
geom_errorbar(aes(ymin = estimate-1.96*se, ymax = estimate+1.96*se), width=0) +
geom_text(y=0.63) +
scale_y_continuous(limits=c(NA,.65), expand=c(0,0)) +
xlab("Spearman correlation") +
ylab("Cell type") +
ggtitle("Concordance between t-statistics: mixed")
rbind(resTab.fixed, resTab.mixed) %>%
as_tibble %>%
mutate( fdr = p.adjust(p.value, "fdr")) %>%
ggplot(aes(assay.x, pi1.rep.y, fill=assay.x, alpha=method)) +
geom_bar(stat="identity", position = position_dodge(width = 0.9)) +
theme_classic() +
theme(aspect.ratio=1, legend.position="none", plot.title = element_text(hjust = 0.5)) +
facet_wrap(~coef.x) +
coord_flip()
```
### Concordance between sets: fixed
Correlation between t-statistics
```{r de.summary.fixed}
library(broom)
coefs = c('Dx2AD', 'Dx2SCZ')
resTab = lapply( coefs, function(key){
tab = lapply( res.dlList.Donor, function(fit){
tab = topTable(fit, coef=key, number=Inf)
tab$key = with(tab, paste(assay, ID, key))
data.frame(coef=key, tab)
})
tab = inner_join(tab[[1]], tab[[2]], by='key')
tab %>%
as_tibble %>%
group_by(assay.x) %>%
group_modify(~ tidy(cor.test(.x$t.x, .x$t.y, method="p"))) %>%
left_join( tab %>% group_by(assay.x) %>% tally) %>%
mutate(coef = key, se = sqrt((1-estimate^2)/(n-2)))
})
resTab.fixed = do.call(rbind, resTab)
resTab.fixed %>%
as_tibble %>%
mutate( fdr = p.adjust(p.value, "fdr")) %>%
ggplot(aes(assay.x, estimate, fill=assay.x, label=ifelse(fdr < 0.05,' #',''))) +
geom_bar(stat="identity") +
theme_classic() +
theme(aspect.ratio=1, legend.position="none", plot.title = element_text(hjust = 0.5)) +
facet_wrap(~coef) +
coord_flip() +
geom_errorbar(aes(ymin = estimate-1.96*se, ymax = estimate+1.96*se), width=0) +
geom_text(y=0.63) +
scale_y_continuous(limits=c(NA,.65), expand=c(0,0)) +
xlab("Spearman correlation") +
ylab("Cell type") +
ggtitle("Concordance between t-statistics: fixed")
```
```{r compare.concordance}
resTab.fixed$method = "fixed"
resTab.mixed$method = "mixed"
df = rbind(resTab.fixed, resTab.mixed)
df %>%
as_tibble %>%
mutate( fdr = p.adjust(p.value, "fdr")) %>%
ggplot(aes(assay.x, estimate, fill=assay.x, label=ifelse(fdr < 0.05,' #',''), alpha=method)) +
geom_bar(stat="identity", position = position_dodge(width = 0.9)) +
theme_classic() +
theme(aspect.ratio=1, legend.position="bottom", plot.title = element_text(hjust = 0.5)) +
facet_wrap(~coef) +
coord_flip() +
geom_errorbar(aes(ymin = pmax(0, estimate-1.96*se), ymax = estimate+1.96*se), width=0, position = position_dodge(width = 0.9)) +
geom_text(y=0.63) +
scale_y_continuous(limits=c(0,.65), expand=c(0,0)) +
xlab("Spearman correlation") +
ylab("Cell type") +
ggtitle("Concordance between t-statistics") +
scale_alpha_manual(values = c(1, .5)) +
guides(fill = FALSE)
# merge(resTab.fixed, resTab.mixed, by="key") %>%
# ggplot(aes(estimate.x, estimate.y, color=assay.x)) +
# geom_abline(linetype="dashed", color="grey50") +
# geom_errorbar(aes(xmin = estimate.x - 1.96*se.x, xmax = estimate.x + 1.96*se.x)) +
# geom_errorbar(aes(ymin = estimate.y - 1.96*se.y, ymax = estimate.y + 1.96*se.y)) +
# geom_point() +
# facet_wrap(~coef) +
# theme_classic() +
# theme(aspect.ratio=1, plot.title = element_text(hjust = 0.5)) +
# coord_fixed(xlim=c(0, .6), ylim=c(0, .6), expand=FALSE) +
# scale_color_discrete(name = "Cell type") +
# xlab("dreamlet: fixed") +
# ylab("dreamlet: mixed")
```
```{r plots}
library(ggplot2)
library(viridis)
# Compute two dimensional density
get_density <- function(x, y, n = 250) {
dens <- MASS::kde2d(x = x, y = y, n = n)
ix <- findInterval(x, dens$x)
iy <- findInterval(y, dens$y)
ii <- cbind(ix, iy)
return(dens$z[ii])
}
# make scatter plot with 2D density using viridis colors
plotScatterDensity = function(value1, value2){
# convert two vectors to a data frame
df = data.frame(cbind(value1, value2))
# determine limits of the plot
lim = with(df, max(abs(c(value1, value2))))
# Compute 2D density
df$density <- get_density(df$value1, df$value2, n = 100)
# Scatter plot colored by density
ggplot(df, aes(value1, value2, color=density)) + geom_point(size=.4) + theme_bw(16) + theme(aspect.ratio=1, legend.position="bottom", plot.title = element_text(hjust = 0.5)) + geom_abline(color="red") + geom_vline(xintercept=0, col="grey40", linetype="dashed") + geom_hline(yintercept=0, col="grey40", linetype="dashed")+ scale_color_viridis() + geom_smooth(method="lm", se=FALSE, color="darkorange") + xlim(-lim, lim) + ylim(-lim, lim)
}
```
```{r concordance.t.stat}
tabA = topTable(res.dlList[['A']], coef='Dx2AD', number=Inf) %>%
as_tibble %>%
filter(assay == "Glutamatergic neuron")
tabB = topTable(res.dlList[['B']], coef='Dx2AD', number=Inf) %>%
as_tibble %>%
filter(assay == "Glutamatergic neuron")
df = inner_join(tabA, tabB, by="ID")
cor(df$t.x, df$t.y, method="sp")
plotScatterDensity(df$t.x, df$t.y)
```
```{r concordance}
# stats
coefs = c('Dx2AD', 'Dx2SCZ')
tab = lapply( names(res.dlList), function(key){
tab = lapply(coefs, function(coef){
tab = topTable(res.dlList[[key]], coef=coef, number=Inf)
tab %>%
as_tibble %>%
group_by(assay) %>%
summarize( nGenes = length(adj.P.Val),
nDE = sum(adj.P.Val < 0.05),
pi1 = get_pi1(P.Value)) %>%
mutate(key = key, coef = coef)
})
do.call(rbind, tab)
})
tab = do.call(rbind, tab)
resTab = lapply( coefs, function(key){
tab = lapply( res.dlList, function(fit){
tab = topTable(fit, coef=key, number=Inf)
tab$key = with(tab, paste(assay, ID, key))
data.frame(coef=key, tab)
})
tab = inner_join(tab[[1]], tab[[2]], by='key')
tab %>%
as_tibble %>%
group_by(assay.x) %>%
summarise(
nGenes = length(adj.P.Val.x),
nDE = sum(adj.P.Val.x < 0.05),
pi1.x = get_pi1(P.Value.x[adj.P.Val.y <= 0.05]),
pi1.y = get_pi1(P.Value.y[adj.P.Val.x <= 0.05])) %>%
mutate(coef = key)
})
resTab = do.call(rbind, resTab)
```
### GC content and batch effects
```{r gc.content}
# Compute GC content
# adapted from https://github.com/dpryan79/Answers/blob/master/SEQanswers_42420/GTF2LengthGC.R
library(GenomicRanges)
library(rtracklayer)
library(Rsamtools)
# CellRanger
# GTFfile = "/hpc/packages/minerva-centos7/cellranger/5.0.0/refdata-cellranger/refdata-gex-GRCh38-2020-A/genes/genes.gtf"
# FASTAfile = "/hpc/packages/minerva-centos7/cellranger/5.0.0/refdata-cellranger/refdata-gex-GRCh38-2020-A/fasta/genome.fa"
GTFfile = "/sc/arion/projects/psychAD/ref_GRCh38/Homo_sapiens.GRCh38.104.gtf"
FASTAfile = "/sc/arion/projects/psychAD/ref_GRCh38/Homo_sapiens.GRCh38.dna.primary_assembly.fa"
GTF <- import.gff(GTFfile, format="gtf", genome="GRCh38.104", feature.type="exon")
grl <- GenomicRanges::reduce(split(GTF, elementMetadata(GTF)$gene_id))
reducedGTF <- unlist(grl, use.names=T)
elementMetadata(reducedGTF)$gene_id <- rep(names(grl), elementNROWS(grl))
# Open the fasta file
FASTA <- FaFile(FASTAfile)
open(FASTA)
#Add the GC numbers
elementMetadata(reducedGTF)$nGCs <- letterFrequency(getSeq(FASTA, reducedGTF), "GC")[,1]
elementMetadata(reducedGTF)$widths <- width(reducedGTF)
#Create a list of the ensembl_id/GC/length
calc_GC_length <- function(x) {
nGCs = sum(elementMetadata(x)$nGCs)
width = sum(elementMetadata(x)$widths)
c(width, nGCs/width)
}
df_GC <- t(sapply(split(reducedGTF, elementMetadata(reducedGTF)$gene_id), calc_GC_length))
colnames(df_GC) <- c("Length", "GC")
df_GC = as.data.frame(df_GC)
```
### Concordance with batch effect in each freeze
```{r examine, fig.width=8, fig.height=14}
library(viridis)
figList = lapply(res.vpList, function(vp){
plotVarPart(sortCols(vp), label.angle=45)
})
figList[[1]] + ggtitle("Freeze 1")
figList[[2]] + ggtitle("Freeze 2")
```
## Batch effects in freezes A and B
```{r compare, fig.width=7, fig.height=10}
df = lapply(names(res.vpList), function(id){
df = data.frame(Set = id, res.vpList[[id]])
df$key = with(df, paste(gene, assay))
df
})
df = merge(df[[1]], df[[2]], by="key")
get_density <- function(x, y, ...) {
dens <- MASS::kde2d(x, y, ...)
ix <- findInterval(x, dens$x)
iy <- findInterval(y, dens$y)
ii <- cbind(ix, iy)
return(dens$z[ii])
}
key = "batch"
a = paste0(key, '.x')
b = paste0(key, '.y')
df$density <- get_density(df[[a]], df[[b]], n = 100)
ggplot(df, aes_string(a,b)) + geom_point(aes(color=density), size=.02) + facet_wrap(~assay.y, ncol=3) + theme_classic() + theme(aspect.ratio=1) + geom_abline(color="grey50", linetype="dashed", width=.3) + scale_color_viridis() + xlab("Freeze A") + ylab("Freeze B")