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sum_longread_final.R
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sum_longread_final.R
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## ---------------------------------------------------------------##
## *** summarize longread seq final result **** ##
## ---------------------------------------------------------------##
source('scDataAnalysis_Utilities.R')
`%notin%` = Negate(`%in%`)
Add_fusionInf2Seurat <- function(sampleName, seurat.rna, nmismatch = 0,
dtype = 'both'){
sampleName1 = sampleName
if(sampleName %in% c('PAZBLA', 'PAYLNH', 'PAYUZJ'))
sampleName1 = paste0(sampleName, '_Redo')
if(sampleName %in% c('HD2111_CD34', 'HD2689_Live'))
sampleName1 = unlist(strsplit(sampleName, '_'))[1]
dir0 = '/mnt/isilon/tan_lab/chenc6/MLLr_Project/scRNA/LongRead/Final_Results/'
read_file = paste0(dir0, sampleName1, '/', sampleName1,
'.fastq.gz.demultiplexing.PASS.reads.txt')
message(paste0(sampleName, ':'))
## read all long reads for used bc
long.res = fread(read_file)
names(long.res)[c(1:3, 7)] = c('readname', 'bc', 'mismatch', 'sec_mismatch')
long.res = long.res[mismatch <= nmismatch]
long.res = subset(long.res, select = c('readname', 'bc', 'mismatch', 'sec_mismatch'))
#long.res = long.res[sec_mismatch > 4]
n0 = nrow(long.res)
## filtered out reads that are not map2 KMT2A PCR region
if(sampleName == 'PAYKGI') return(seurat.rna)
reads.pcr = fread(paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_reads2PCR.txt'))
long.res = long.res[readname %in% reads.pcr$readname, ]
message(paste0(round(nrow(long.res)/n0 * 100, 1), '% reads in PCR!'))
long.res$bc = paste0(sampleName, '_', long.res$bc)
long.res[, 'nLongread' := .N, by = bc]
## note the bc with longread data
tmp_long = subset(long.res, select = c(nLongread, bc)) %>% .[!duplicated(.)]
setkey(tmp_long, bc)
tmp_mdata = data.table('bc' = rownames([email protected][seurat.rna$sample == sampleName, ]))
tmp_mdata[, 'nLongread' := tmp_long[J(tmp_mdata$bc)]$nLongread]
tmp_mdata$nLongread[is.na(tmp_mdata$nLongread)] = 0
seurat.rna$nLongread[tmp_mdata$bc] = tmp_mdata$nLongread
long.res = long.res[bc %in% colnames(seurat.rna)]
## read fusion reads from alignqc
if(dtype != 'longGF'){
fusion.file.alignqc = paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_filtered_fusion.txt')
if(!file.exists(fusion.file.alignqc)) return(seurat.rna)
fusion_reads.alignqc = unique(fread(fusion.file.alignqc)$readname)
long.fusion.alignqc = long.res[readname %in% fusion_reads.alignqc & bc %in% colnames(seurat.rna)]
message(paste('AlignQC detected', nrow(long.fusion.alignqc), 'fusion reads in',
length(unique(long.fusion.alignqc$bc)), 'cells!'))
}
## read fusion reads from longGF
if(dtype != 'alignqc'){
fusion.files = dir(paste0(dir0, sampleName1))
fusion.file.sele = fusion.files[grepl(fusion.files, pattern = 'KMT2A|AP001267')]
fusion.file.sele = fusion.file.sele[grepl(fusion.file.sele, pattern = 'list$')]
fusion.file.sele = fusion.file.sele[grepl(fusion.file.sele, pattern = 'filtered.readname', fixed = T)]
fusion_reads.longgf = lapply(fusion.file.sele, function(x) fread(paste0(dir0, sampleName1, '/',
x), header = F)$V1)
fusion_reads.longgf = unique(do.call('c', fusion_reads.longgf))
long.fusion.longgf = long.res[readname %in% fusion_reads.longgf & bc %in% colnames(seurat.rna)]
message(paste('longGF detected', nrow(long.fusion.longgf), 'fusion reads in',
length(unique(long.fusion.longgf$bc)), 'cells!'))
}
if(dtype == 'alignqc') long.fusion = long.fusion.alignqc
if(dtype == 'longGF') long.fusion = long.fusion.longgf
if(dtype == 'both') {
long.fusion = long.fusion.alignqc[readname %in% long.fusion.longgf$readname]
message(paste('Detected', nrow(long.fusion), 'fusion reads in',
length(unique(long.fusion$bc)), 'cells by both methods!'))
}
message('-----------------------------------')
if(nrow(long.fusion) == 0) return(seurat.rna)
## write final read inf
mdata0 = data.table([email protected], keep.rownames = T)
mdata0$Ctype0[mdata0$Ctype0 %in% c('T', 'NKT')] = 'T/NK'
setkey(mdata0, rn)
long.fusion[, 'Ctype0' := mdata0[J(long.fusion$bc)]$Ctype0]
write.table(subset(long.fusion[nLongread >= 1], select = c('readname', 'bc', 'Ctype0')),
file = paste0('MetaData/scRNA/LongRead/Final_Fusions/', sampleName, '.txt'),
row.names = F, quote = F, sep = '\t')
long.res.sum = subset(long.fusion, select = c(bc))
long.res.sum[, 'nfusion' := .N, by = bc]
long.res.sum = long.res.sum[!duplicated(long.res.sum)]
## update detect infromation into seurat
fusion_name = fusionParters[sampleName]
seurat.rna$detected_fusion <- ifelse(colnames(seurat.rna) %in% long.res.sum$bc,
fusion_name, seurat.rna$detected_fusion)
seurat.rna$detected_nfusion[long.res.sum$bc] = long.res.sum$nfusion
return(seurat.rna)
}
Add_wtInf2Seurat <- function(sampleName, seurat.rna, nmismatch = 0,
dtype = 'both'){
sampleName1 = sampleName
if(sampleName %in% c('PAZBLA', 'PAYLNH', 'PAYUZJ'))
sampleName1 = paste0(sampleName, '_Redo')
if(sampleName %in% c('HD2111_CD34', 'HD2689_Live'))
sampleName1 = unlist(strsplit(sampleName, '_'))[1]
message(paste0(sampleName, ':'))
dir0 = '/mnt/isilon/tan_lab/chenc6/MLLr_Project/scRNA/LongRead/Final_Results/'
read_file = paste0(dir0, sampleName1, '/', sampleName1,
'.fastq.gz.demultiplexing.PASS.reads.txt')
## read all long reads for used bc
long.res = fread(read_file)
names(long.res)[c(1:3, 7)] = c('readname', 'bc', 'mismatch', 'sec_mismatch')
long.res = long.res[mismatch <= nmismatch]
long.res = subset(long.res, select = c('readname', 'bc', 'mismatch', 'sec_mismatch'))
#long.res = long.res[sec_mismatch > 4]
n0 = nrow(long.res)
## filtered out reads that are not map2 KMT2A PCR region
if(sampleName == 'PAYKGI') return(seurat.rna)
reads.pcr = fread(paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_reads2PCR.txt'))
long.res = long.res[readname %in% reads.pcr$readname, ]
#message(paste0(round(nrow(long.res)/n0 * 100, 1), '% reads in PCR!'))
long.res$bc = paste0(sampleName, '_', long.res$bc)
long.res = long.res[bc %in% colnames(seurat.rna)]
## read wt reads from alignqc
if(dtype != 'longGF'){
wt.file.alignqc = paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_filtered_wt.txt')
if(!file.exists(wt.file.alignqc)) return(seurat.rna)
wt_reads.alignqc = unique(fread(wt.file.alignqc)$readname)
long.wt.alignqc = long.res[readname %in% wt_reads.alignqc & bc %in% colnames(seurat.rna)]
message(paste('AlignQC detected', nrow(long.wt.alignqc), 'wt reads in',
length(unique(long.wt.alignqc$bc)), 'cells!'))
}
## read wt reads from longGF
if(dtype != 'alignqc'){
wt_reads.longgf = fread(paste0(dir0, sampleName1, '/',
sampleName1, '.fastq.wt_reads.list'), header = F)$V1
long.wt.longgf = long.res[readname %in% wt_reads.longgf & bc %in% colnames(seurat.rna)]
message(paste('longGF detected', nrow(long.wt.longgf), 'wt reads in',
length(unique(long.wt.longgf$bc)), 'cells!'))
}
if(dtype == 'alignqc') long.wt = long.wt.alignqc
if(dtype == 'longGF') long.wt = long.wt.longgf
if(dtype == 'both') {
long.wt = long.wt.alignqc[readname %in% long.wt.longgf$readname]
message(paste('Detected', nrow(long.wt), 'wt reads in',
length(unique(long.wt$bc)), 'cells by both methods!'))
}
message('-----------------------------------')
if(nrow(long.wt) == 0) return(seurat.rna)
long.res.sum = subset(long.wt, select = c(bc))
long.res.sum[, 'nwt' := .N, by = bc]
long.res.sum = long.res.sum[!duplicated(long.res.sum)]
## update detect infromation into seurat
seurat.rna$detected_wt <- ifelse(colnames(seurat.rna) %in% long.res.sum$bc,
'WT', seurat.rna$detected_wt)
seurat.rna$detected_nwt[long.res.sum$bc] = long.res.sum$nwt
## write final read inf
mdata0 = data.table([email protected], keep.rownames = T)
mdata0$Ctype0[mdata0$Ctype0 %in% c('T', 'NKT')] = 'T/NK'
setkey(mdata0, rn)
long.wt[, 'Ctype0' := mdata0[J(long.wt$bc)]$Ctype0]
write.table(subset(long.wt, select = c('readname', 'bc', 'Ctype0')),
file = paste0('MetaData/scRNA/LongRead/Final_Fusions/', sampleName, '_wt.txt'),
row.names = F, quote = F, sep = '\t')
return(seurat.rna)
}
fusionRate_HD <- function(sampleName, dtype = 'both'){
dir0 = '/mnt/isilon/tan_lab/chenc6/MLLr_Project/scRNA/LongRead/Final_Results/'
sampleName1 = unlist(strsplit(sampleName, '_'))[1]
read_file = paste0(dir0, sampleName1, '/', sampleName1,
'.fastq.gz.demultiplexing.PASS.reads.txt')
## read all long reads for used bc
long.res = fread(read_file)
names(long.res)[c(1:3, 7)] = c('readname', 'bc', 'mismatch', 'sec_mismatch')
long.res = long.res[mismatch <= nmismatch]
long.res = subset(long.res, select = c('readname', 'bc', 'mismatch', 'sec_mismatch'))
#long.res = long.res[sec_mismatch > 4]
## filtered out reads that are not map2 KMT2A PCR region
reads.pcr = fread(paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_reads2PCR.txt'))
long.res = long.res[readname %in% reads.pcr$readname, ]
message(paste0(round(nrow(long.res)/n0 * 100, 1), '% reads in PCR!'))
message(paste0(sampleName, ':'))
## read fusion reads from longGF
if(dtype != 'alignqc'){
fusion.files = dir(paste0(dir0, sampleName1))
fusion.file.sele = fusion.files[grepl(fusion.files, pattern = 'KMT2A|AP001267')]
fusion.file.sele = fusion.file.sele[grepl(fusion.file.sele, pattern = 'list$')]
fusion.file.sele = fusion.file.sele[grepl(fusion.file.sele, pattern = 'readname', fixed = T)]
fusion_reads.longgf = lapply(fusion.file.sele, function(x) fread(paste0(dir0, sampleName1, '/',
x), header = F)$V1)
fusion_reads.longgf = unique(do.call('c', fusion_reads.longgf))
long.fusion.longgf = long.res[readname %in% fusion_reads.longgf ]
message(paste('longGF detected', nrow(long.fusion.longgf), 'fusion reads in',
length(unique(long.fusion.longgf$bc)), 'cells!'))
}
## read fusion reads from alignqc
if(dtype != 'longGF'){
fusion.file.alignqc = paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_filtered_fusion.txt')
if(!file.exists(fusion.file.alignqc)) return(seurat.rna)
fusion_reads.alignqc = unique(fread(fusion.file.alignqc)$readname)
long.fusion.alignqc = long.res[readname %in% fusion_reads.alignqc]
message(paste('AlignQC detected', nrow(long.fusion.alignqc), 'fusion reads in',
length(unique(long.fusion.alignqc$bc)), 'cells!'))
}
if(dtype == 'alignqc') long.fusion = long.fusion.alignqc
if(dtype == 'longGF') long.fusion = long.fusion.longgf
if(dtype == 'both') {
long.fusion = long.fusion.alignqc[readname %in% long.fusion.longgf$readname]
message(paste('Detected', nrow(long.fusion), 'fusion reads in',
length(unique(long.fusion$bc)), 'cells by both methods!'))
}
return(unique(long.fusion$bc))
}
wtRate_HD <- function(sampleName, dtype = 'both'){
dir0 = '/mnt/isilon/tan_lab/chenc6/MLLr_Project/scRNA/LongRead/Final_Results/'
sampleName1 = unlist(strsplit(sampleName, '_'))[1]
read_file = paste0(dir0, sampleName1, '/', sampleName1,
'.fastq.gz.demultiplexing.PASS.reads.txt')
## read all long reads for used bc
long.res = fread(read_file)
names(long.res)[c(1:3, 7)] = c('readname', 'bc', 'mismatch', 'sec_mismatch')
long.res = long.res[mismatch <= nmismatch]
long.res = subset(long.res, select = c('readname', 'bc', 'mismatch', 'sec_mismatch'))
#long.res = long.res[sec_mismatch > 4]
## filtered out reads that are not map2 KMT2A PCR region
if(sampleName == 'PAYKGI') return(seurat.rna)
reads.pcr = fread(paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_reads2PCR.txt'))
long.res = long.res[readname %in% reads.pcr$readname, ]
message(paste0(round(nrow(long.res)/n0 * 100, 1), '% reads in PCR!'))
message(paste0(sampleName, ':'))
if(dtype != 'longGF'){
wt.file.alignqc = paste0('MetaData/scRNA/LongRead/', sampleName, '_alignqc_filtered_wt.txt')
if(!file.exists(wt.file.alignqc)) return(seurat.rna)
wt_reads.alignqc = unique(fread(wt.file.alignqc)$readname)
long.wt.alignqc = long.res[readname %in% wt_reads.alignqc ]
message(paste('AlignQC detected', nrow(long.wt.alignqc), 'wt reads in',
length(unique(long.wt.alignqc$bc)), 'cells!'))
}
## read wt reads from longGF
if(dtype != 'alignqc'){
wt_reads.longgf = fread(paste0(dir0, sampleName1, '/',
sampleName1, '.fastq.wt_reads.list'), header = F)$V1
long.wt.longgf = long.res[readname %in% wt_reads.longgf]
message(paste('longGF detected', nrow(long.wt.longgf), 'wt reads in',
length(unique(long.wt.longgf$bc)), 'cells!'))
}
if(dtype == 'alignqc') long.wt = long.wt.alignqc
if(dtype == 'longGF') long.wt = long.wt.longgf
if(dtype == 'both') {
long.wt = long.wt.alignqc[readname %in% long.wt.longgf$readname]
message(paste('Detected', nrow(long.wt), 'wt reads in',
length(unique(long.wt$bc)), 'cells by both methods!'))
}
return(unique(long.wt$bc))
}
plot_detectionRate <- function(mdata, do.test = F, test.bg = 'Mature_B',
type.read = 'fusion'){
mdata$Ctype0[mdata$Ctype0 %in% c('T', 'NKT')] = 'T/NK'
n_ctype = table(mdata$Ctype0)
mdata0 = mdata[mdata$detected_nfusion > 0, ]
if(type.read == 'wt') mdata0 = mdata[mdata$detected_nwt > 0, ]
if(is.null(mdata0) | nrow(mdata0) < 1) return(NULL)
n_detected = table(mdata0$Ctype0)
ctype.not.detected = setdiff(names(n_ctype), names(n_detected))
n_detected[ctype.not.detected] = 0
n_ctype = n_ctype[names(n_detected)]
data_fusion_ctype = data.table('Ctype' = names(n_ctype),
'nCtype' = as.vector(n_ctype),
'nDetected' = as.vector(n_detected))
data_fusion_ctype[, 'rate' := nDetected/nCtype]
data_fusion_ctype = data_fusion_ctype[Ctype != 'pDC']
pvs = rep(1, nrow(data_fusion_ctype))
names(pvs) = data_fusion_ctype$Ctype
if(do.test){
for(type0 in data_fusion_ctype$Ctype){
if(type0 == test.bg) next
pvs[type0] = pbinom(data_fusion_ctype[Ctype == type0]$nDetected,
data_fusion_ctype[Ctype == type0]$nCtype,
prob = data_fusion_ctype[Ctype == test.bg]$rate,
lower.tail = F)
pvs[type0] = min(pvs[type0], 1-pvs[type0])
}
data_fusion_ctype$pv = paste0('p=', format(pvs, digit = 2))
data_fusion_ctype[Ctype == test.bg]$pv = 'background'
}
data_fusion_ctype$rate = round(data_fusion_ctype$rate, 4)
data_fusion_ctype0 = data_fusion_ctype
#data_fusion_ctype$Ctype = factor(data_fusion_ctype$Ctype,
# levels = c('Blasts', 'Progenitors',
# 'T/NK', 'Monocytes', 'Mature_B'))
p1 <- ggplot(data = data_fusion_ctype, aes(x = Ctype, y = rate, fill = Ctype)) +
geom_bar(stat = 'identity') + NoLegend() +
xlab('') + ylab('Fraction')
if(do.test) p1 = p1 + geom_text(aes(label = pv, x = Ctype, y = rate+0.001))
return(list('plot' = p1, 'pdata' = data_fusion_ctype0))
}
## add fusion inf to seurat ####
seurat.rna = readRDS(file = 'Seurat_Objects/scRNA/seurat_regrCycleHeatShockGenes_pool_18Infants_scRNA_VEG3000_updated.rds' )
sampleNames = c("PAYYBG", "PAZBSZ", "PAZFPH", "PAZGKI",
"PAYWKL", "PAYYNY", "PAYZWN ", "PAYUZM",
"154", "PAYSBA", "PAYWJZ", "PAZBLA",
"PAYKGI", "PAYLNH", "PAYUZJ", "PAYZLC",
"PAYZVY", "PAZBGV")
fusionParters = c('MLLT1', 'MLLT1', 'MLLT1', 'MLLT3',
'AFF1', 'AFF1', 'AFF1', 'MLLT10',
'MLLT3', 'AFF1', 'MLLT1', 'MLLT10',
'AFF1', 'EPS15', 'MLLT1', 'EPS15',
'PSMF1', 'MLLT3')
names(fusionParters) = sampleNames
seurat.rna$detected_wt = 'NA'
seurat.rna$detected_nwt = 0
seurat.rna$detected_fusion = 'NA'
seurat.rna$detected_nfusion = 0
seurat.rna$nLongread = 0
detect_method = 'both'
for(name0 in sampleNames){
seurat.rna <- Add_fusionInf2Seurat(name0, seurat.rna, 0, detect_method)
seurat.rna <- Add_wtInf2Seurat(name0, seurat.rna, 0, detect_method)
}
#saveRDS(seurat.rna, file = 'Seurat_Objects/scRNA/seurat_regrCycleHeatShockGenes_pool_18Infants_scRNA_VEG3000_updated.rds' )
mdata.all = data.table([email protected], keep.rownames = T)
mdata.all = mdata.all[Ctype_Final %notin% c('doublets', 'Unknown')]
saveRDS(mdata.all, file='MetaData/scRNA/LongRead/mdata_with_longread_inf.rds')
## for HD ####
seurat.hd <- readRDS('Seurat_Objects/scRNA/seurat_pool_logNorm_gini_FiveHD_10Xv3_downsample10000HSPC.rds')
seurat.hd = subset(seurat.hd, sample %in% c('donor2111_CD34p', 'donor2689_live'))
cnames = colnames(seurat.hd)
cnames = sapply(cnames, function(x) gsub('donor2111_CD34p', 'HD2111_CD34', x))
cnames = sapply(cnames, function(x) gsub('donor2689_live', 'HD2689_Live', x))
names(cnames) = NULL
samples = ifelse(grepl(cnames, pattern = 'HD2111_CD34'), 'HD2111_CD34', 'HD2689_Live')
seurat.hd$sample = samples
seurat.hd = RenameCells(seurat.hd, new.names = cnames)
seurat.hd$detected_fusion = 'NA'
seurat.hd$detected_nfusion = 0
seurat.hd$detected_wt = 'NA'
seurat.hd$detected_nwt = 0
seurat.hd$nLongread = 0
fusionParters[c('HD2111_CD34', 'HD2689_Live')] = 'DETECTED'
seurat.hd <- Add_fusionInf2Seurat('HD2111_CD34', seurat.hd, 0, 'both')
seurat.hd <- Add_wtInf2Seurat('HD2111_CD34', seurat.hd, 0, 'both')
seurat.hd <- Add_fusionInf2Seurat('HD2689_Live', seurat.hd, 0, 'both')
seurat.hd <- Add_wtInf2Seurat('HD2689_Live', seurat.hd, 0, 'both')
saveRDS(seurat.hd, file = 'Seurat_Objects/scRNA/hd_wLongread.rds')
mdata.hd = data.table([email protected], keep.rownames = T)
mdata.hd[, 'nLongread_str' := detected_nfusion + detected_nwt]
mdata.hd.pcr = mdata.hd[nLongread_str >= 1]
f2111 <- nrow(mdata.hd.pcr[detected_nfusion > 0 & sample == 'HD2111_CD34'])
w2111 <- nrow(mdata.hd.pcr[detected_nwt > 0 & sample == 'HD2111_CD34'])
f2689 <- nrow(mdata.hd.pcr[detected_nfusion > 0 & sample == 'HD2689_Live'])
w2689 <- nrow(mdata.hd.pcr[detected_nwt > 0 & sample == 'HD2689_Live'])
n2111 = nrow(mdata.hd.pcr[sample == 'HD2111_CD34'])
n2689 = nrow(mdata.hd.pcr[sample == 'HD2689_Live'])
c(f2111, w2111)/n2111
c(f2689, w2689)/n2689
hdata <- data.table('perc' = c(c(f2111, w2111)/n2111, c(f2689, w2689)/n2689) *100,
'sample' = rep(c('2111_CD34', '2689_Live'), each=2),
'read.type' = rep(c('Fusion', 'WT'), 2))
hdata$perc = round(hdata$perc, 2)
ggplot(hdata, aes(x=sample, y=perc, fill = read.type)) +
geom_bar(stat = 'identity', position='dodge') + xlab('') +
ylab('Detection rate: Percentage') +
geom_text(aes(label=paste0(perc, '%')), vjust=-0.5,
size=3.5, hjust = 0.5) + theme_classic() +
theme(legend.title = element_blank())
## pool all data ####
mdata.all = readRDS(file='MetaData/scRNA/LongRead/mdata_with_longread_inf.rds')
mdata.all$Ctype0[mdata.all$Ctype0 %in% c('T', 'NKT')] = 'T/NK'
mdata.all[, 'nLongread_str' := detected_nfusion + detected_nwt]
mdata.all = mdata.all[sample != 'PAZGKI']
mdata.working = mdata.all[nLongread_str > 0]
#mdata.working = mdata.all[nLongread > 0]
res1 = plot_detectionRate(mdata.working, do.test = F, type.read = 'fusion')
res2 = plot_detectionRate(mdata.working, do.test = F, type.read = 'wt')
dd1 = res1$pdata
dd2 = res2$pdata
dd1$read.type = 'Fusion'
dd2$read.type = 'WT'
dd_fusion_wt <- rbind(dd1, dd2)
pdata = subset(dd_fusion_wt, select = c('Ctype', 'rate', 'read.type'))
data.comb = pdata
data.comb$perc = round(data.comb$rate, 4) * 100
data.comb = data.comb[Ctype != 'Normal']
#data.comb$Ctype = factor(data.comb$Ctype, levels = c('Blasts', 'Progenitors',
# '2111_CD34', '2689_Live'))
p0 <- ggplot(data = data.comb, aes(x = Ctype, y = rate, fill = read.type)) +
geom_bar(stat = 'identity', position='dodge') +
xlab('') + ylab('Fraction') +
scale_fill_manual(values = brewer.pal(5, name = 'Set1')[1:2]) +
geom_text(aes(label=paste0(perc, '%')), vjust=-0.5,
size=3.5, hjust = 0.5) +
theme_classic()
## combine pt and hd data
pdata = subset(dd_fusion_wt, select = c('Ctype', 'rate', 'read.type'))
hdata = subset(hdata, select = c('sample', 'perc', 'read.type'))
names(hdata) = names(pdata)
hdata$rate = hdata$rate/100
data.comb = rbind(pdata, hdata)
data.comb$perc = round(data.comb$rate, 4) * 100
data.comb = data.comb[Ctype != 'Normal']
data.comb$Ctype = factor(data.comb$Ctype, levels = c('Blasts', 'Progenitors',
'2111_CD34', '2689_Live'))
p7 <- ggplot(data = data.comb, aes(x = Ctype, y = rate, fill = read.type)) +
geom_bar(stat = 'identity', position='dodge') +
xlab('') + ylab('Fraction') +
scale_fill_manual(values = brewer.pal(5, name = 'Set1')[1:2]) +
geom_text(aes(label=paste0(perc, '%')), vjust=-0.5,
size=3.5, hjust = 0.5) +
theme_classic()
ggsave(p7, filename = paste0('Figures/summary/LongRead/ExactMatch/fusionANDwt_rate_barplot_by_',
detect_method, '.eps'),
device = 'eps', width = 6, height = 6)
## using all cells as denominator -- final ####
seurat.hd <- readRDS(file = 'Seurat_Objects/scRNA/hd_wLongread.rds')
mdata.hd = data.table([email protected], keep.rownames = T)
mdata.hd.pcr = mdata.hd[nLongread >= 0]
f2111 <- nrow(mdata.hd.pcr[detected_nfusion > 0 & sample == 'HD2111_CD34'])
w2111 <- nrow(mdata.hd.pcr[detected_nwt > 0 & sample == 'HD2111_CD34'])
f2689 <- nrow(mdata.hd.pcr[detected_nfusion > 0 & sample == 'HD2689_Live'])
w2689 <- nrow(mdata.hd.pcr[detected_nwt > 0 & sample == 'HD2689_Live'])
n2111 = nrow(mdata.hd.pcr[sample == 'HD2111_CD34'])
n2689 = nrow(mdata.hd.pcr[sample == 'HD2689_Live'])
c(f2111, w2111)/n2111
c(f2689, w2689)/n2689
hdata <- data.table('rate' = c(c(f2111, w2111)/n2111, c(f2689, w2689)/n2689) ,
'Ctype' = rep(c('2111_CD34', '2689_Live'), each=2),
'read.type' = rep(c('Fusion', 'WT'), 2))
hdata = subset(hdata, select = c(Ctype, rate, read.type))
mdata.all = readRDS(file='MetaData/scRNA/LongRead/mdata_with_longread_inf.rds')
mdata.all$Ctype0[mdata.all$Ctype0 %in% c('T', 'NKT')] = 'T/NK'
mdata.all[, 'nLongread_str' := detected_nfusion + detected_nwt]
mdata.all = mdata.all[sample != 'PAZGKI']
progenitor.g <- c('PAZBLA', 'PAYUZJ', 'PAYLNH',
'1154', 'PAYWKL', 'PAYKGI',
'PAYUZM', 'PAYSBA', 'PAZGKI')
nprog.g = setdiff(sampleNames, progenitor.g)
mdata.working = mdata.all
res1 = plot_detectionRate(mdata.working, do.test = F, type.read = 'fusion')
res2 = plot_detectionRate(mdata.working, do.test = F, type.read = 'wt')
dd1 = res1$pdata
dd2 = res2$pdata
dd1$read.type = 'Fusion'
dd2$read.type = 'WT'
dd_fusion_wt <- rbind(dd1, dd2)
pdata = subset(dd_fusion_wt, select = c('Ctype', 'rate', 'read.type'))
data.comb = rbind(pdata, hdata)
data.comb$perc = round(data.comb$rate, 4) * 100
data.comb$Ctype = factor(data.comb$Ctype, levels = c('Blasts', 'Progenitors',
'Mature_B', 'Monocytes', 'T/NK',
'2111_CD34', '2689_Live'))
p1 <- ggplot(data = data.comb, aes(x = Ctype, y = rate, fill = read.type)) +
geom_bar(stat = 'identity', position='dodge') +
xlab('') + ylab('Fraction') +
scale_fill_manual(values = brewer.pal(5, name = 'Set1')[1:2]) +
geom_text(aes(label=paste0(perc, '%')), vjust=-0.5,
size=3.5, hjust = 0.5) +
theme_classic()
ggsave(p1, filename = 'Figures/summary/LongRead/ExactMatch/updated_frac_fusion_wt_allCells.eps',
device = 'eps', width = 6, height = 6)