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compTFenrich_18MLLr.R
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compTFenrich_18MLLr.R
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## compare TF enrichment between patients and HD
source('scDataAnalysis_Utilities.R')
filterTFenrichByExpr <- function(tf.diff.res, expr.mtx,
frac.thr = 0.1){
cls = unique(tf.diff.res$cluster1)
res = NULL
for(cl0 in cls){
tf.diff.res0 = tf.diff.res[cluster1 == cl0]
frac0 = expr.mtx[, cl0]
hfrac.gene0 = names(which(frac0 > frac.thr))
res = rbind(res, tf.diff.res0[feature %in% hfrac.gene0])
}
return(res)
}
## plot enriched tf for chromvar, color by cluster
plot_enrich_tf0 <- function(sele.zscores, bc_clusters,
cluster.levels = NULL,
up.qt = 0.95, low.qt = 0.05,
ndownsample = 1000){
bc_clusters = data.table(bc_clusters)
#downsample
set.seed(2019)
bc_clusters.down = bc_clusters
if(!is.null(ndownsample) & ndownsample < nrow(bc_clusters.down))
bc_clusters.down = bc_clusters.down[sort(sample((1:nrow(bc_clusters.down)), ndownsample)), ]
bc_clusters = bc_clusters.down
rr = bc_clusters$barcode[bc_clusters$barcode %in% colnames(sele.zscores)]
sele.zscores = sele.zscores[, rr]
ann_column = data.frame('cluster' = as.character(bc_clusters$cluster),
'barcode' = bc_clusters$barcode,
stringsAsFactors = F)
rownames(ann_column) = bc_clusters$barcode
up_cut = quantile(sele.zscores, up.qt, na.rm = T)
low_cut = quantile(sele.zscores, low.qt, na.rm = T)
sele.zscores[is.na(sele.zscores)] = 0
low_cut = min(0, low_cut)
sele.zscores[sele.zscores > up_cut] = up_cut
sele.zscores[sele.zscores < low_cut] = low_cut
nc = length(unique(bc_clusters$cluster))
getPalette = colorRampPalette(brewer.pal(9, "Paired"))
if(nc >= 3) color_cluster = getPalette(nc)
if(nc < 3) color_cluster = c("#A6CEE3", "#1F78B4", "#B2DF8A")[1:nc]
names(color_cluster) = sort(unique(bc_clusters$cluster))
ann_column = ann_column[order(ann_column$cluster), ]
if(is.null(cluster.levels)) {
ann_column = ann_column[order(factor(ann_column$cluster,
levels = sort(unique(ann_column$cluster)))), ]
}else{
ann_column = ann_column[order(factor(ann_column$cluster,
levels = cluster.levels)), ]
color_cluster = color_cluster[cluster.levels]
}
ann_colors = list('cluster' = color_cluster)
sele.zscores = sele.zscores[, ann_column$barcode]
ann_column$barcode = NULL
ph <- pheatmap::pheatmap(sele.zscores,
cluster_cols = F, cluster_rows = F,
show_colnames = F, fontsize = 9,
annotation_col = ann_column,
color = viridis(100),
annotation_colors = ann_colors,
fontsize_row = 9)
return(ph)
}
## virids style, downsample, match_cell, colomn color by sample/cluster
plot_enrich_tf1 <- function(sele.tfs, zscore.all, bc_clusters,
cluster.levels = NULL,
cluster.col = NULL,
up.qt = 0.95, low.qt = 0.05,
ndownsample = 2000, match_cell = F,
reScale = F, cluster.rows = F,
color_style = 'virid'){
sele.zscores = zscore.all[sele.tfs, ]
if(reScale) sele.zscores = t(scale(t(sele.zscores), center = T, scale = T))
bc_clusters = data.table(bc_clusters)
#downsample and match_cell
ncell.cl = min(table(bc_clusters$cluster))
set.seed(2020)
bc_clusters.down = bc_clusters
if(match_cell){
bc_clusters.down = NULL
for(cl0 in unique(bc_clusters$cluster)){
tmp = bc_clusters[cluster == cl0]
if(nrow(tmp) > ncell.cl) tmp = tmp[sort(sample((1:nrow(tmp)), ncell.cl)), ]
bc_clusters.down = rbind(bc_clusters.down, tmp)
}
}
if(!is.null(ndownsample) & ndownsample < nrow(bc_clusters.down))
bc_clusters.down = bc_clusters.down[sort(sample((1:nrow(bc_clusters.down)), ndownsample)), ]
bc_clusters = bc_clusters.down
rr = bc_clusters$barcode[bc_clusters$barcode %in% colnames(sele.zscores)]
sele.zscores = sele.zscores[, rr]
ann_column = data.frame('cluster' = bc_clusters$cluster,
'barcode' = bc_clusters$barcode,
stringsAsFactors = F)
rownames(ann_column) = bc_clusters$barcode
up_cut = quantile(sele.zscores, up.qt, na.rm = T)
low_cut = quantile(sele.zscores, low.qt, na.rm = T)
sele.zscores[is.na(sele.zscores)] = 0
low_cut = min(0, low_cut)
sele.zscores[sele.zscores > up_cut] = up_cut
sele.zscores[sele.zscores < low_cut] = low_cut
nc = length(unique(bc_clusters$cluster))
getPalette = colorRampPalette(brewer.pal(9, "Paired"))
if(is.null(cluster.col)){
if(nc >= 3) color_cluster = getPalette(nc)
if(nc < 3) color_cluster = c("#A6CEE3", "#1F78B4", "#B2DF8A")[1:nc]
names(color_cluster) = sort(unique(bc_clusters$cluster))
}else{
color_cluster = cluster.col
}
## order
ann_column = ann_column[order(factor(ann_column$cluster,
levels = sort(unique(ann_column$cluster)))), ]
if(is.null(cluster.levels)) {
ann_column = ann_column[order(factor(ann_column$cluster,
levels = sort(unique(ann_column$cluster)))), ]
}else{
ann_column = ann_column[order(factor(ann_column$cluster,
levels = cluster.levels)), ]
color_cluster = color_cluster[cluster.levels]
}
ann_colors = list('cluster' = color_cluster)
sele.zscores = sele.zscores[, ann_column$barcode]
ann_column$barcode <- NULL
color_fun = viridis(100)
if(color_style == 'purple-yellow') color_fun = PurpleAndYellow()
ph <- pheatmap::pheatmap(sele.zscores, cluster_cols = F,
cluster_rows = cluster.rows,
show_colnames = F, fontsize = 10,
annotation_col = ann_column,
color = color_fun,
annotation_colors = ann_colors,
fontsize_row = 12)
return(ph)
}
## virids style, downsample, match_cell, column color by sample & cluster
plot_enrich_tf2 <- function(sele.tfs, zscore.all, bc_clusters,
cluster.levels = NULL, sample.levels = NULL,
up.qt = 0.95, low.qt = 0.05,
ndownsample = 2000,
match_cell = F, reScale = F,
cluster.rows = F,
color_style = 'virid',
order.within = 'sample'){
sele.zscores = zscore.all[sele.tfs, ]
if(reScale) sele.zscores = t(scale(t(sele.zscores), center = T, scale = T))
bc_clusters = data.table(bc_clusters)
#downsample and match_cell
ncell.cl = min(table(bc_clusters$sample))
set.seed(2020)
bc_clusters.down = bc_clusters
if(match_cell){
bc_clusters.down = NULL
for(sample0 in unique(bc_clusters$sample)){
tmp = bc_clusters[sample == sample0]
if(nrow(tmp) > ncell.cl) tmp = tmp[sort(sample((1:nrow(tmp)), ncell.cl)), ]
bc_clusters.down = rbind(bc_clusters.down, tmp)
}
}
if(!is.null(ndownsample) & ndownsample < nrow(bc_clusters.down))
bc_clusters.down = bc_clusters.down[sort(sample((1:nrow(bc_clusters.down)), ndownsample)), ]
bc_clusters = bc_clusters.down
rr = bc_clusters$barcode[bc_clusters$barcode %in% colnames(sele.zscores)]
sele.zscores = sele.zscores[, rr]
ann_column = data.frame('cluster' = bc_clusters$cluster,
'barcode' = bc_clusters$barcode,
'sample' = bc_clusters$sample,
stringsAsFactors = F)
rownames(ann_column) = bc_clusters$barcode
up_cut = quantile(sele.zscores, up.qt, na.rm = T)
low_cut = quantile(sele.zscores, low.qt, na.rm = T)
sele.zscores[is.na(sele.zscores)] = 0
low_cut = min(0, low_cut)
sele.zscores[sele.zscores > up_cut] = up_cut
sele.zscores[sele.zscores < low_cut] = low_cut
nc = length(unique(bc_clusters$cluster))
getPalette = colorRampPalette(brewer.pal(9, "Paired"))
if(nc >= 3) color_cluster = getPalette(nc)
if(nc < 3) color_cluster = c("#A6CEE3", "#1F78B4", "#B2DF8A")[1:nc]
names(color_cluster) = sort(unique(bc_clusters$cluster))
nsample = length(unique(bc_clusters$sample))
getPalette = colorRampPalette(brewer.pal(9, "Paired"))
if(nsample < 3) color_sample = c("#A6CEE3", "#1F78B4", "#B2DF8A")[1:nsample]
if(nsample > 3) color_sample = getPalette(nsample)
names(color_sample) = sample.levels
## order sample by age
ann_column = ann_column[order(factor(ann_column$sample,
levels = sample.levels[sample.levels %in% ann_column$sample])), ]
ann_column = ann_column[order(factor(ann_column$cluster,
levels = sort(unique(ann_column$cluster)))), ]
if(order.within == 'sample'){
if(!is.null(cluster.levels)) {
ann_column = ann_column[order(factor(ann_column$cluster,
levels = cluster.levels)), ]
color_cluster = color_cluster[cluster.levels]
}
if(!is.null(sample.levels)){
ann_column = ann_column[order(factor(ann_column$sample,
levels = sample.levels)), ]
color_sample = color_sample[sample.levels]
}
}else{
if(!is.null(sample.levels)){
ann_column = ann_column[order(factor(ann_column$sample,
levels = sample.levels)), ]
color_sample = color_sample[sample.levels]
}
if(!is.null(cluster.levels)) {
ann_column = ann_column[order(factor(ann_column$cluster,
levels = cluster.levels)), ]
color_cluster = color_cluster[cluster.levels]
}
}
ann_colors = list('cluster' = color_cluster,
'sample' = color_sample)
sele.zscores = sele.zscores[, ann_column$barcode]
ann_column$barcode <- NULL
color_fun = viridis(100)
if(color_style == 'purple-yellow') color_fun = PurpleAndYellow()
ph <- pheatmap::pheatmap(sele.zscores, cluster_cols = F,
cluster_rows = cluster.rows,
show_colnames = F, fontsize = 10,
annotation_col = ann_column,
color = color_fun,
annotation_colors = ann_colors,
fontsize_row = 12)
return(ph)
}
## virids style, downsample, match_cell, colomn color by a continous variable (time)
## and a cluster/sample
plot_enrich_tf3 <- function(sele.tfs, zscore.all, bc_clusters,
cluster.levels = NULL, cluster.col = NULL,
up.qt = 0.95, low.qt = 0.05,
ndownsample = 2000,
match_cell = F, reScale = F,
cluster.rows = F,
color_style = 'virid',
order.withinCls = F){
sele.zscores = zscore.all[sele.tfs, ]
sele.zscores = sele.zscores[, bc_clusters$barcode]
if(reScale) sele.zscores = t(scale(t(sele.zscores), center = T, scale = T))
bc_clusters = data.table(bc_clusters)
#downsample and match_cell
ncell.cl = min(table(bc_clusters$cluster))
set.seed(2020)
bc_clusters.down = bc_clusters
if(match_cell){
bc_clusters.down = NULL
for(cl0 in unique(bc_clusters$cluster)){
tmp = bc_clusters[cluster == cl0]
if(nrow(tmp) > ncell.cl) tmp = tmp[sort(sample((1:nrow(tmp)), ncell.cl)), ]
bc_clusters.down = rbind(bc_clusters.down, tmp)
}
}
if(!is.null(ndownsample) & ndownsample < nrow(bc_clusters.down))
bc_clusters.down = bc_clusters.down[sort(sample((1:nrow(bc_clusters.down)), ndownsample)), ]
bc_clusters = bc_clusters.down
rr = bc_clusters$barcode[bc_clusters$barcode %in% colnames(sele.zscores)]
sele.zscores = sele.zscores[, rr]
ann_column = data.frame('cluster' = bc_clusters$cluster,
'barcode' = bc_clusters$barcode,
'pseudotime' = bc_clusters$pseudotime,
stringsAsFactors = F)
rownames(ann_column) = bc_clusters$barcode
up_cut = quantile(sele.zscores, up.qt, na.rm = T)
low_cut = quantile(sele.zscores, low.qt, na.rm = T)
sele.zscores[is.na(sele.zscores)] = 0
low_cut = min(0, low_cut)
sele.zscores[sele.zscores > up_cut] = up_cut
sele.zscores[sele.zscores < low_cut] = low_cut
if(is.null(cluster.col)){
nc = length(unique(bc_clusters$cluster))
getPalette = colorRampPalette(brewer.pal(9, "Paired"))
if(nc >= 3) color_cluster = getPalette(nc)
if(nc < 3) color_cluster = c("#A6CEE3", "#1F78B4", "#B2DF8A")[1:nc]
names(color_cluster) = sort(unique(bc_clusters$cluster))
}else{
color_cluster = cluster.col
}
ann_column = ann_column[order(ann_column$pseudotime), ]
if(order.withinCls & !is.null(cluster.levels)){
color_cluster = color_cluster[cluster.levels]
tmp = NULL
for(cl0 in cluster.levels){
ann_column0 = ann_column[ann_column$cluster == cl0, ]
ann_column0 = ann_column0[order(ann_column0$pseudotime), ]
tmp = rbind(tmp, ann_column0)
}
ann_column = tmp
rownames(ann_column) = ann_column$barcode
ann_column$cluster = factor(ann_column$cluster, levels = cluster.levels)
}
## order by psudotime
ann_colors = list('cluster' = color_cluster)
sele.zscores = sele.zscores[, ann_column$barcode]
ann_column$barcode <- NULL
color_fun = viridis(100)
if(color_style == 'purple-yellow') color_fun = PurpleAndYellow()
ph <- pheatmap::pheatmap(sele.zscores, cluster_cols = F,
cluster_rows = cluster.rows,
show_colnames = F, fontsize = 10,
annotation_col = ann_column,
color = color_fun,
annotation_colors = ann_colors,
fontsize_row = 12)
return(ph)
}
## prepare gene-by-group freq of expressed cells ####
seurat.rna.hd <- readRDS('Seurat_Objects/scRNA/seurat_pool_logNorm_gini_FiveHD_10Xv3_downsample10000HSPC.rds')
mtx = seurat.rna.hd@assays$RNA@counts
Ctypes = seurat.rna.hd$Ctype
efreq.ctype.hd <- sapply(unique(Ctypes), function(x) {
cl_data <- mtx[, Ctypes == x]
Matrix::rowMeans(cl_data > 0)
})
efreq.earlyprog.hd <- rowMeans(mtx[, Ctypes == 'HSPC' | Ctypes == 'LMPP'] > 0)
rm(seurat.rna.hd, mtx)
seurat.rna.mllr = readRDS('Seurat_Objects/scRNA/seurat_regrCycleHeatShockGenes_pool_18Infants_scRNA_VEG3000_updated.rds')
mtx.mllr = seurat.rna.mllr[['RNA']]@counts
Ctype0 = seurat.rna.mllr$Ctype0
Ctype0[Ctype0 %in% c('NKT', 'T')] = 'T/NK'
pCtypes = seurat.rna.mllr$projCtype
efreq.ctype.mllr <- sapply(unique(pCtypes), function(x) {
cl_data <- mtx.mllr[, Ctype0 == 'Blasts' & pCtypes == x]
Matrix::rowMeans(cl_data > 0)
})
efreq.ctype0.mllr <- sapply(unique(Ctype0), function(x) {
cl_data <- mtx.mllr[, Ctype0 == x]
Matrix::rowMeans(cl_data > 0)
})
efreq.hspc1 = rowMeans(mtx.mllr[, Ctype0 == 'Progenitors'] > 0)
efreq.earlyprog.mllr = rowMeans(mtx.mllr[, Ctype0 == 'Blasts' & (pCtypes == 'HSPC-like' | pCtypes == 'LMPP-like')] > 0)
rm(seurat.rna.mllr, mtx.mllr)
## save data for future usage
save(efreq.ctype.hd, efreq.earlyprog.hd, file = 'MetaData/scRNA/efreq_ctypte_hd.RData')
save(efreq.ctype.mllr, efreq.hspc1, efreq.earlyprog.mllr,
efreq.ctype0.mllr, file = 'MetaData/scRNA/efreq_ctypte_mllr.RData')
## prepare dscore and zscore ####
if(F){
chrvar.all <- readRDS('chromVAR_Objects/chromVAR_MLLrwithPubAtac_usePeaks4TFcomp_allPeaks.rds')
zscore.all = chrvar.all@assays@data$z
dscore.all = chrvar.all@assays@data$deviations
## change to readable name
rnames = rownames(zscore.all)
nnames = sapply(rnames, function(x) unlist(strsplit(x, '_'))[3])
nnames1 = sapply(rnames, function(x) unlist(strsplit(x, '_'))[1])
rownames(zscore.all) = ifelse(grepl(nnames, pattern = 'LINE'),
nnames1, nnames)
rownames(dscore.all) = rownames(zscore.all)
## ENSG00000187728 -- TCF24
save(zscore.all, dscore.all,
file = 'chromVAR_Objects/zscore_dscore_all.RData')
save(zscore.all, dscore.all,
file = 'chromVAR_Objects/zscore_dscore_all_allPeaks.RData')
}
## add metadata for each cell ####
load('chromVAR_Objects/zscore_dscore_all_allPeaks.RData')
seurat.hd <- readRDS('Seurat_Objects/scATAC/seurat_9HDsamplesPlusThreeHD_TFIDF_usingDAPs1.rds')
seurat.mllr <- readRDS('Seurat_Objects/scATAC/seurat_pool_18MLLr_TFIDF_vap10000.rds')
ann.hd = data.table([email protected], keep.rownames = T)
ann.mllr = data.table([email protected], keep.rownames = T)
ann.hd = subset(ann.hd, select = c('rn', 'sample', 'seurat_ctype', 'pseudotime'))
ann.mllr = subset(ann.mllr, select = c('rn', 'sample', 'projCtype', 'projCtype_merge', 'Ctype0', 'pseudotime'))
ann.hd[, 'bc_name' := gsub('.1', '', rn, fixed = T), by = rn]
ann.hd[, 'bc_name' := gsub('.2', '', bc_name, fixed = T), by = rn]
ann.hd[, 'bc_name' := gsub('Pub3_', '', bc_name, fixed = T), by = rn]
ann.hd[, 'sample_cell' := paste0(sample, bc_name)]
ann.hd = subset(ann.hd, select = c('bc_name', 'sample', 'seurat_ctype', 'sample_cell',
'pseudotime'))
names(ann.hd)[3] = 'Ctype'
fwrite(ann.hd, file = paste0('MetaData/scATAC/hd_bc_sample_inf.txt'),
sep = '\t', quote = F)
ann.mllr$bc_name = sapply(ann.mllr$rn, function(x) substr(x, nchar(x)-15, nchar(x)))
ann.mllr = subset(ann.mllr, select = c('bc_name', 'sample', 'projCtype', 'projCtype_merge', 'Ctype0',
'pseudotime'))
ann.mllr[, 'sample_cell' := paste0(sample, bc_name)]
fwrite(ann.mllr, file = paste0('MetaData/scATAC/mllr18Infants_bc_sample_inf.txt'),
sep = '\t', quote = F)
## compare blast projected cell type with corresponding normal cell types ####
load('chromVAR_Objects/zscore_dscore_all.RData')
ann.hd = fread('MetaData/scATAC/hd_bc_sample_inf.txt')
ann.mllr = fread('MetaData/scATAC/mllr18Infants_bc_sample_inf.txt')
ann.hspc1 = ann.mllr[Ctype0 == 'Progenitors']
b.types = c('HSPC', 'LMPP', 'CLP', 'Pre-pro-B', 'Pro-B', 'Pre-B', 'Immature-B',
'Mature-B')
b.types.like = paste0(b.types, '-like')
pairs.b.ctype = paste0(b.types, '_vs_', b.types.like)
pairs.other = c('HSPC1_vs_HSPC', 'EarlyProg_vs_EarlyProg-like')
res.diff = list()
for(pair0 in c(pairs.b.ctype, pairs.other)){
if(pair0 %in% pairs.b.ctype){
ctype0 = unlist(strsplit(pair0, split = '_vs_'))[1]
ctype1 = paste0(ctype0, '-like')
cls = rep(c(ctype0, ctype1),
c(nrow(ann.hd[Ctype == ctype0]), nrow(ann.mllr[projCtype == ctype1 & Ctype0 == 'Blasts'])))
cls = data.table('barcode' = c(ann.hd[Ctype == ctype0]$sample_cell,
ann.mllr[projCtype == ctype1 & Ctype0 == 'Blasts']$sample_cell),
'cluster' = cls)
}
if(pair0 == 'HSPC1_vs_HSPC'){
cls = rep(c('HSPC', 'HSPC1'),
c(nrow(ann.hd[Ctype == 'HSPC']), nrow(ann.hspc1)))
cls = data.table('barcode' = c(ann.hd[Ctype == 'HSPC']$sample_cell,
ann.hspc1$sample_cell),
'cluster' = cls)
}
if(pair0 == 'HSPC1_vs_HSPC-like'){
cls = rep(c('HSPC-like', 'HSPC1'),
c(nrow(ann.mllr[Ctype0 == 'Blasts' & projCtype == 'HSPC-like']),
nrow(ann.hspc1)))
cls = data.table('barcode' = c(ann.mllr[Ctype0 == 'Blasts' & projCtype == 'HSPC-like']$sample_cell,
ann.hspc1$sample_cell),
'cluster' = cls)
}
if(pair0 == 'HSPC1_vs_EarlyProg-like'){
cls = rep(c('HSPC1', 'EarlyProg-like'),
c(nrow(ann.hspc1),
nrow(ann.mllr[Ctype0 == 'Blasts' & projCtype %in% paste0(c('HSPC','LMPP'), '-like')])))
cls = data.table('barcode' = c(ann.hspc1$sample_cell,
ann.mllr[Ctype0 == 'Blasts' & projCtype %in%
paste0(c('HSPC','LMPP'), '-like')]$sample_cell),
'cluster' = cls)
}
if(pair0 == 'HSPC1_vs_EarlyProg'){
cls = rep(c('HSPC1', 'EarlyProg'),
c(nrow(ann.hspc1), nrow(ann.hd[Ctype %in% c('HSPC', 'LMPP')])))
cls = data.table('barcode' = c(ann.hspc1$sample_cell,
ann.hd[Ctype %in% c('HSPC', 'LMPP')]$sample_cell),
'cluster' = cls)
}
if(pair0 == 'EarlyProg_vs_EarlyProg-like'){
cls = rep(c('EarlyProg', 'EarlyProg-like'),
c(nrow(ann.hd[Ctype %in% c('HSPC', 'LMPP')]),
nrow(ann.mllr[Ctype0 == 'Blasts' & projCtype %in% paste0(c('HSPC','LMPP'), '-like')])))
cls = data.table('barcode' = c(ann.hd[Ctype %in% c('HSPC', 'LMPP')]$sample_cell,
ann.mllr[Ctype0 == 'Blasts' & projCtype %in%
paste0(c('HSPC','LMPP'), '-like')]$sample_cell),
'cluster' = cls)
}
res.diff[[pair0]] <- runDiffMotifEnrich(dscore.all, clusters = cls, fdr = 0.05,
max_cell_per_clust = 2000,
topn = 200, min_frac_per_cluster = 0.2)
rm(cls)
}
## filtered enriched list by expression
load('MetaData/scRNA/efreq_ctypte_hd.RData')
load('MetaData/scRNA/efreq_ctypte_mllr.RData')
res.diff.filtered = list()
expr_cutoff = 0.2
for(pair0 in c(pairs.b.ctype, pairs.other)){
res.diff0 = res.diff[[pair0]]
sele.tfs0 = sapply(unique(res.diff0$cluster1), function(x){
res.diff0 = res.diff0[order(-mean1)]
tfs = res.diff0[cluster1 == x]$feature
if(x %in% colnames(efreq.ctype.mllr)) efreq0 = efreq.ctype.mllr[, x]
if(x %in% colnames(efreq.ctype.hd)) efreq0 = efreq.ctype.hd[, x]
if(x == 'HSPC1') efreq0 = efreq.hspc1
if(x == 'EarlyProg') efreq0 = efreq.earlyprog.hd
if(x == 'EarlyProg-like') efreq0 = efreq.earlyprog.mllr
## filter by expr frac
tfs = tfs[tfs %in% names(efreq0)]
tfs = tfs[efreq0[tfs] > expr_cutoff]
return(tfs)
})
if(class(sele.tfs0) == 'list') sele.tfs0 = do.call('c', sele.tfs0)
res.diff.filtered[[pair0]] = res.diff0[feature %in% sele.tfs0]
}
if(F){
## use less stringent rule to check more on the list
res.diff.filtered = list()
expr_cutoff = 0.05
for(pair0 in c(pairs.b.ctype, pairs.other)){
res.diff0 = res.diff[[pair0]]
sele.tfs0 = sapply(unique(res.diff0$cluster1), function(x){
res.diff0 = res.diff0[order(-mean1)]
tfs = res.diff0[cluster1 == x]$feature
if(x %in% colnames(efreq.ctype.mllr)) efreq0 = efreq.ctype.mllr[, x]
if(x %in% colnames(efreq.ctype.hd)) efreq0 = efreq.ctype.hd[, x]
if(x == 'HSPC1') efreq0 = efreq.hspc1
if(x == 'EarlyProg') efreq0 = efreq.earlyprog.hd
if(x == 'EarlyProg-like') efreq0 = efreq.earlyprog.mllr
## filter by expr frac
tfs = tfs[tfs %in% names(efreq0)]
tfs = tfs[efreq0[tfs] > expr_cutoff]
return(tfs)
})
if(class(sele.tfs0) == 'list') sele.tfs0 = do.call('c', sele.tfs0)
res.diff.filtered[[pair0]] = res.diff0[feature %in% sele.tfs0]
}
#output_dir = paste0('Figures/scATAC/TF_Enrich/MLLr18Infants/filteredByExpr_frac',
# expr_cutoff)
dir.create(output_dir, showWarnings = F)
blasts.diff = res.diff.filtered[1:8]
blasts.diff.up = lapply(blasts.diff, function(x) x[grepl(cluster1, pattern = 'like')])
blasts.diff.down = lapply(blasts.diff, function(x) x[!grepl(cluster1, pattern = 'like')])
blasts.diff.up = do.call('rbind', blasts.diff.up)
blasts.diff.down = do.call('rbind', blasts.diff.down)
}
## commonly enriched in blast cells
output_dir = paste0('Figures/scATAC/TF_Enrich/MLLr18Infants/filteredByExpr_frac',
expr_cutoff)
dir.create(output_dir, showWarnings = F)
blasts.diff = res.diff.filtered[1:8]
blasts.diff.up = lapply(blasts.diff, function(x) x[grepl(cluster1, pattern = 'like')])
blasts.diff.down = lapply(blasts.diff, function(x) x[!grepl(cluster1, pattern = 'like')])
blasts.diff.up = do.call('rbind', blasts.diff.up)
blasts.diff.down = do.call('rbind', blasts.diff.down)
up.tfs = sort(table(blasts.diff.up$feature), decreasing = T)
down.tfs = sort(table(blasts.diff.down$feature), decreasing = T)
# plot a barplot for top TFs
if(T){
tf_freq.up = data.table('TF' = names(up.tfs),
'N' = as.numeric(up.tfs))
tf_freq.down = data.table('TF' = names(down.tfs),
'N' = as.numeric(down.tfs))
tf_freq.up = tf_freq.up[order(-N), ]
tf_freq.up = tf_freq.up[N >= 3]
tf_freq.up = tf_freq.up[order(N), ]
tf_freq.up$TF = factor(tf_freq.up$TF, levels = tf_freq.up$TF)
p_up <- ggplot(tf_freq.up, aes(y = N, x = TF)) +
geom_bar(width = 0.7, stat = 'identity', fill = '#1F78B4') +
ggtitle("Commonly Enriched TF in Blasts") + theme_classic() +
theme(legend.position = 'bottom', legend.direction = "horizontal") +
coord_flip() +
xlab('') + ylab('# of Stages')
tf_freq.down = tf_freq.down[N >= 3]
tf_freq.down = tf_freq.down[order(N), ]
tf_freq.down$TF = factor(tf_freq.down$TF, levels = tf_freq.down$TF)
p_down <- ggplot(tf_freq.down, aes(y = N, x = TF)) +
geom_bar(width = 0.7, stat = 'identity', fill = '#1F78B4') +
ggtitle("Commonly Depleted TF in Blasts") + theme_classic() +
theme(legend.position = 'bottom', legend.direction = "horizontal") +
coord_flip() +
xlab('') + ylab('# of Stages')
ggsave(p_up, filename = paste0(output_dir, '/Commonly_Enriched_TFs_InBlasts.eps'),
width = 6, height = 7, device = 'eps')
ggsave(p_down, filename = paste0(output_dir, '/Commonly_Depleted_TFs_InBlasts.eps'),
width = 6, height = 10, device = 'eps')
saveRDS(tf_freq.up, file = paste0(output_dir, '/commonly_enriched_tfs.rds'))
saveRDS(tf_freq.down, file = paste0(output_dir, '/commonly_depleted_tfs.rds'))
}
## plot TFs heatmap commonly enriched in blast vs normal ####
up.tfs = names(which(up.tfs > 3))
down.tfs = names(which(down.tfs > 3))
sele.tfs = c(up.tfs, down.tfs)
cls = data.table('barcode' = c(ann.hd$sample_cell, ann.mllr$sample_cell),
'cluster' = c(ann.hd$Ctype, ann.mllr$projCtype),
'sample' = rep(c('HD', 'Blasts'), c(nrow(ann.hd), nrow(ann.mllr))))
ordered.clusters = c(b.types[1:8], paste0(b.types[1:8], '-like'))
ordered.sample = c('HD', 'Blasts')
cls = cls[cluster %in% ordered.clusters ]
ph <- plot_enrich_tf2(sele.tfs, zscore.all, bc_clusters = cls,
up.qt = 0.96,
ndownsample = 1000,
cluster.levels = ordered.clusters, cluster.rows = T,
sample.levels = ordered.sample, reScale = T,
match_cell = T, color_style = 'purple-yellow')
#ggsave(ph, filename = paste0(output_dir, '/TF_enrichedIn4Stages_blastVSnormal_purpleyellow.eps'),
# width = 12, height = 12, device = 'eps')
ggsave(ph, filename = paste0(output_dir, '/TF_enrichedIn4Stages_blastVSnormal_purpleyellow_v2.eps'),
width = 12, height = 12, device = 'eps')
## plot HSPC vs HSPC1 ####
hspc.diff = res.diff.filtered[['HSPC1_vs_HSPC']]
setkey(hspc.diff, cluster1, pv_adjust)
cls = data.table('barcode' = c(ann.hd[Ctype == 'HSPC']$sample_cell,
ann.hspc1$sample_cell),
'cluster' = c(ann.hd[Ctype == 'HSPC']$Ctype,
rep('HSPC1', nrow(ann.hspc1))),
'sample' = c(rep('HD', nrow(ann.hd[Ctype == 'HSPC'])),
ann.hspc1$sample))
ordered.sample = c('HD', "PAYWJZ", "PAZGKI", "PAYUZM", "1154",
"PAYKGI", "PAZBSZ", "PAYWKL", "PAYSBA",
"PAZBLA", "PAYZLC", "PAYLNH", "PAYUZJ",
"PAYYBG", "PAZFPH", "PAYYNY", "PAZBGV",
"PAYZVY", "PAYZWN ")
ordered.clusters = c('HSPC', 'HSPC1')
hspc.diff[, 'delta' := -1*(mean1 - mean0)/mean1]
setkey(hspc.diff, cluster1, delta)
hspc.diff$delta = -hspc.diff$delta
hspc.diff = hspc.diff[mean1 > 0.012]
ph.py <- plot_enrich_tf1(hspc.diff$feature, zscore.all, bc_clusters = cls,
up.qt = 0.97,
ndownsample = 1000, match_cell = T,
cluster.levels = ordered.clusters, reScale = T,
color_style = 'purple-yellow')
ggsave(ph.py, filename = paste0(output_dir, '/enriched_TFs_HSPCvsHSPC1_purpleyellow.eps'),
width = 12, height = 12, device = 'eps')
## comparing blast with normal cells in patients ####
## prepare dscore and zscore
if(F){
chrvar.mllr <- readRDS('chromVAR_Objects/chromVAR_pool_18MLLr_TFIDF_allPeaks.rds')
zscore.mllr = chrvar.mllr@assays@data$z
dscore.mllr = chrvar.mllr@assays@data$deviations
## change to readable name
rnames = rownames(zscore.mllr)
nnames = sapply(rnames, function(x) unlist(strsplit(x, '_'))[3])
nnames1 = sapply(rnames, function(x) unlist(strsplit(x, '_'))[1])
rownames(zscore.mllr) = ifelse(grepl(nnames, pattern = 'LINE'),
nnames1, nnames)
rownames(dscore.mllr) = rownames(zscore.mllr)
## ENSG00000187728 -- TCF24
#save(zscore.mllr, dscore.mllr,
# file = 'chromVAR_Objects/zscore_dscore_18MLLr.RData')
save(zscore.mllr, dscore.mllr,
file = 'chromVAR_Objects/zscore_dscore_18MLLr_allPeaks.RData')
}
load('chromVAR_Objects/zscore_dscore_18MLLr_allPeaks.RData')
ann.mllr = fread('MetaData/scATAC/mllr18Infants_bc_sample_inf.txt')
load('MetaData/scRNA/efreq_ctypte_mllr.RData')
ctype0.count = table(ann.mllr$Ctype0)
cls = subset(ann.mllr, select = c(Ctype0, sample_cell, sample))
names(cls) = c('cluster', 'barcode', 'sample')
dscore.mllr[is.na(dscore.mllr)] = 0
## change cell names to be consistent
cnames = colnames(dscore.mllr)
cnames = sapply(cnames, function(x) gsub('_scATAC', '', x))
cnames = sapply(cnames, function(x) gsub('MLL_', 'MLLr', x))
all(cnames %in% cls$barcode)
colnames(dscore.mllr) = cnames
colnames(zscore.mllr) = cnames
cls = cls[barcode %in% cnames]
tf.mllr <- runDiffMotifEnrich(dscore.mllr, clusters = cls, fdr = 0.05,
max_cell_per_clust = 2000,
topn = 200, min_frac_per_cluster = 0.2)
if(F){
## relax TFs for GRN
expr_cutoff = 0.1
topn = 20
tf.mllr.sele = tf.mllr[cluster1 %in% c('Blasts', 'Progenitors', 'Mature_B',
'T/NK', 'Monocytes')]
sele.tfs0 = lapply(c('Blasts', 'Progenitors', 'Mature_B',
'T/NK', 'Monocytes'), function(x){
res.diff0 = tf.mllr.sele[order(-mean1)]
res.diff0 = res.diff0[cluster1 == x]
tfs = res.diff0[cluster1 == x]$feature
efreq0 = efreq.ctype0.mllr[, x]
## filter by expr frac
tfs = tfs[tfs %in% names(efreq0)]
tfs = tfs[efreq0[tfs] > expr_cutoff]
tfs = res.diff0[1:topn, ]
return(tfs)
})
names(sele.tfs0) = c('Blasts', 'Progenitors', 'Mature_B',
'T/NK', 'Monocytes')
saveRDS(sele.tfs0, file = paste0('EP_Prediction/Ctype0_enriched_tfs_exprfrac0.1.rds'))
}
expr_cutoff = 0.2
tf.mllr.sele = tf.mllr[cluster1 %in% c('Blasts')]
sele.tfs0 = sapply(c('Blasts'), function(x){
res.diff0 = tf.mllr.sele[order(-mean1)]
tfs = res.diff0[cluster1 == x]$feature
efreq0 = efreq.ctype0.mllr[, x]
## filter by expr frac
tfs = tfs[tfs %in% names(efreq0)]
tfs = tfs[efreq0[tfs] > expr_cutoff]
return(tfs)
})
if(class(sele.tfs0) == 'list') sele.tfs0 = do.call('c', sele.tfs0)
sele.tfs0 = unique(sele.tfs0)
tf.mllr.sele = tf.mllr.sele[feature %in% sele.tfs0]
tf.mllr.sele[, 'delta' := (mean1 - mean0)/mean1]
tf.mllr.sele = tf.mllr.sele[order(-delta), ]
cls = cls[cluster != 'pDC']
ordered.sample = c("PAYWJZ", "PAZGKI", "PAYUZM", "1154",
"PAYKGI", "PAZBSZ", "PAYWKL", "PAYSBA",
"PAZBLA", "PAYZLC", "PAYLNH", "PAYUZJ",
"PAYYBG", "PAZFPH", "PAYYNY", "PAZBGV",
"PAYZVY", "PAYZWN ")
ph <- plot_enrich_tf2(tf.mllr.sele$feature, zscore.mllr,
bc_clusters = cls,
up.qt = 0.96,
ndownsample = 1000, match_cell = F,
cluster.levels = c('Progenitors', 'Blasts',
'Mature_B', 'Monocytes',
'T/NK'),
sample.levels = ordered.sample,
reScale = T, cluster.rows = T,
order.within = 'cluster',
color_style = 'purple-yellow')
ggsave(ph, filename = paste0(output_dir, '/enriched_TFs_Blast_Normal.eps'),
width = 12, height = 12, device = 'eps')
## show top TFs for all ctype0 ####
ann.mllr = fread('MetaData/scATAC/mllr18Infants_bc_sample_inf.txt')
load('MetaData/scRNA/efreq_ctypte_mllr.RData')
ctype0.count = table(ann.mllr$Ctype0)
expr_cutoff = 0.2
output_dir = paste0('Figures/scATAC/TF_Enrich/MLLr18Infants/filteredByExpr_frac',
expr_cutoff)
tf.mllr.sele = tf.mllr
topn = 15
## plot evenly across cell types
sele.tfs0 = lapply(c('Progenitors', 'Blasts',
'Mature_B', 'Monocytes',
'T/NK'), function(x){
res.diff0 = tf.mllr.sele[cluster1 == x]
res.diff0 = res.diff0[order(-mean1)]
efreq0 = efreq.ctype0.mllr[, x]
## filter by expr frac
res.diff0 = res.diff0[feature %in% names(efreq0)]
tfs = res.diff0$feature
tfs.expr = names(which(efreq0[tfs] > expr_cutoff))
res.diff0 = res.diff0[feature %in% tfs.expr]
if(nrow(res.diff0) > topn) res.diff0 = res.diff0[1:topn, ]
return(res.diff0)
})
names(sele.tfs0) = c('Progenitors', 'Blasts',
'Mature_B', 'Monocytes',
'T/NK')
saveRDS(sele.tfs0, file = paste0(output_dir, '/Ctype0_top_enriched_tfs.rds'))
if(class(sele.tfs0) == 'list') sele.tfs = do.call('rbind', sele.tfs0)
sele.tfs = sele.tfs$feature
sele.tfs = unique(c(sele.tfs0[['Progenitors']]$feature, sele.tfs0[['Blasts']]$feature,
sele.tfs0[['Mature_B']]$feature, sele.tfs0[['Monocytes']]$feature))
sele.tfs = c(setdiff(sele.tfs, c('ELF1', 'ETS1', 'ELK4')), sele.tfs0[['T/NK']]$feature)
cls_color <- c( "Blasts" = "#E3191C", "Monocytes" = "#347EB5",
"Mature_B" = "#4CAC47", "T/NK" = "#A35728", "Progenitors" = "#924C9F")
ph <- plot_enrich_tf1(unique(sele.tfs), zscore.mllr,
bc_clusters = cls,
up.qt = 0.96, low.qt = 0.1,
ndownsample = 1000, match_cell = T,
cluster.levels = c('Progenitors', 'Blasts',
'Mature_B', 'Monocytes',
'T/NK'),
cluster.col = cls_color,
reScale = T, cluster.rows = F,
color_style = 'purple-yellow')
ggsave(ph, filename = paste0(output_dir, '/enriched_TFs_Ctype0.eps'),
width = 14, height = 14, device = 'eps')
## plot use real cell type composition
sele.tfs = unique(c(sele.tfs0[['Blasts']]$feature, sele.tfs0[['T/NK']]$feature,
sele.tfs0[['Mature_B']]$feature, sele.tfs0[['Monocytes']]$feature,
sele.tfs0[['Progenitors']]$feature))
ph1 <- plot_enrich_tf1(unique(sele.tfs), zscore.mllr,
bc_clusters = cls,
up.qt = 0.96,
ndownsample = 1000, match_cell = F,
cluster.levels = c( 'Blasts', 'T/NK',
'Mature_B', 'Monocytes',
'Progenitors'),
cluster.col = cls_color,
reScale = T, cluster.rows = F,
color_style = 'purple-yellow')
ggsave(ph1, filename = paste0(output_dir, '/enriched_TFs_Ctype0_unevenComposition.eps'),
width = 14, height = 14, device = 'eps')
## comparing blasts within patients among merged projected cell type ####
## prepare Early-prog -like expression fraction
## Early-prog-like: HSPC-like, LMPP-like, CLP-like
seurat.rna.mllr = readRDS('Seurat_Objects/scRNA/seurat_regrCycleHeatShockGenes_pool_18Infants_scRNA_VEG3000_updated.rds')
mtx.mllr = seurat.rna.mllr[['RNA']]@counts
tmp = subset(seurat.rna.mllr, Ctype_Stage %in% c('HSPC-like', 'LMPP-like', 'CLP-like'))
efreq.earlyprog.mllr1 = rowMeans(tmp@assays$RNA@counts > 0)
load('chromVAR_Objects/zscore_dscore_18MLLr_allPeaks.RData')
ann.mllr = fread('MetaData/scATAC/mllr18Infants_bc_sample_inf.txt')
load('MetaData/scRNA/efreq_ctypte_mllr.RData')
ann.mllr = ann.mllr[Ctype0 == 'Blasts']
b.types = paste0(c('HSPC', 'LMPP', 'CLP', 'Pre-pro-B', 'Pre-B', 'Pro-B',
'Immature-B', 'Mature-B'), '-like')
ann.mllr = ann.mllr[projCtype %in% b.types]
ann.mllr[, 'mergedCtype' := ifelse(projCtype %in% c('HSPC-like', 'LMPP-like', 'CLP-like'),
'Early-Prog-like', projCtype)]
cls = subset(ann.mllr, select = c(mergedCtype, sample_cell, pseudotime))
names(cls) = c('cluster', 'barcode', 'pseudotime')
cls = cls[order(pseudotime)]
dscore.mllr[is.na(dscore.mllr)] = 0
## change cell names to be consistent
cnames = colnames(dscore.mllr)
cnames = sapply(cnames, function(x) gsub('_scATAC', '', x))
cnames = sapply(cnames, function(x) gsub('MLL_', 'MLLr', x))
colnames(dscore.mllr) = cnames
colnames(zscore.mllr) = cnames
cls = cls[barcode %in% cnames]
tf.mllr <- runDiffMotifEnrich(dscore.mllr, clusters = cls, fdr = 0.05,
max_cell_per_clust = 2000,
topn = 200, min_frac_per_cluster = 0.2)
expr_cutoff = 0.2
topn = 15
output_dir = paste0('Figures/scATAC/TF_Enrich/MLLr18Infants/filteredByExpr_frac',
expr_cutoff)
sele.tfs0 = lapply(c('Early-Prog-like', 'Pre-pro-B-like', 'Pro-B-like',
'Pre-B-like', 'Immature-B-like', 'Mature-B-like'), function(x){
res.diff0 = tf.mllr[cluster1 == x]
res.diff0 = res.diff0[order(-mean1)]
if(x == 'Early-Prog-like'){
efreq0 = efreq.earlyprog.mllr1
}else{
efreq0 = efreq.ctype.mllr[, x]
}
## filter by expr frac
res.diff0 = res.diff0[feature %in% names(efreq0)]
tfs = res.diff0$feature
tfs.expr = names(which(efreq0[tfs] > expr_cutoff))
res.diff0 = res.diff0[feature %in% tfs.expr]
if(nrow(res.diff0) > topn) res.diff0 = res.diff0[1:topn, ]
return(res.diff0)
})
names(sele.tfs0) = c('Early-Prog-like', 'Pre-pro-B-like', 'Pro-B-like',
'Pre-B-like', 'Immature-B-like', 'Mature-B-like')
if(F){
## select less stringent TF for constructing GRN
expr_cutoff = 0.1
topn = 20