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Commit b0fbe4ac authored by Nicolò Gualandi's avatar Nicolò Gualandi
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Plot.R 0 → 100644
View file @ b0fbe4ac
#import libraries
library(ggVennDiagram)
library(stringr)
library(RColorBrewer)
library(ggplot2)
library(dplyr)
library(openxlsx)
library(gtable)
library(reshape2)
library(grid)
library(gridExtra)
library(CMplot)
library(circlize)
library(e1071)
library(VennDiagram)
library(scales)
library(lme4)
library(ggrepel)
library(readxl)
################################################### Functions #############################################################
snv_colors2 <- function(del = FALSE, alpha = 1) {
nuc <- c("A", "C", "G", "T")
# Point deletions
dels <- alpha(brewer.pal(5, "Greys")[2:5], 1)
names(dels) <- paste0(nuc, "*")
# Transitions
ts <- alpha(brewer.pal(5, "Blues")[2:5], 1)
names(ts) <- c("CT", "GA", "AG", "TC")
# Tansversions
tv1 <- alpha(brewer.pal(5, "YlGn")[2:5], 1)
names(tv1) <- c("AC", "TG", "AT", "TA")
tv2 <- alpha(brewer.pal(5, "OrRd")[2:5], 1)
names(tv2) <- c("CA", "GT", "CG", "GC")
var_cols <- c(ts, tv1, tv2)
if (del) {
var_cols <- c(var_cols, dels)
}
return(var_cols)
}
### Circos + Legend ###
legend_plot = function(t, sample) {
t <- mutate(t, TYPE = case_when(nchar(REF) == 1 & nchar(ALT) == 1 & ALT != "*" ~ "Substitution",
nchar(REF) == 1 & nchar(ALT) == 1 & ALT == "*" ~ "Deletion",
nchar(REF) > nchar(ALT) & nchar(REF) - nchar(ALT) >= 1 ~ "Deletion",
nchar(REF) < nchar(ALT) & nchar(ALT) - nchar(REF) >= 1 ~ "Insertion",
TRUE ~ "Other"))
# Substitutions
t.subs <- t %>% filter(TYPE == "Substitution") %>% tbl_df()
t.subs$SUB <- paste0(t.subs$REF, t.subs$ALT)
t.subs$SUB <- factor(t.subs$SUB, levels = sort(unique(t.subs$SUB)))
cols_names <- levels(t.subs$SUB)
cols <- snv_colors2(del = F)
t.subs <- merge(t.subs, data.frame(cols, SUB = names(cols)), all.x = TRUE)
tt.subs <- t.subs %>% filter(Sample == sample) %>% tbl_df()
# Deletions / Insertions
t.other <- t %>% filter(TYPE != "Substitution") %>% tbl_df()
tt.other <- t.other %>% filter(Sample == sample) %>% tbl_df()
if(nrow(tt.other) > 0) {
tt.other$AF <- 1
}
tt.other$col <- ifelse(tt.other$TYPE == "Deletion", "Red", "Blue")
# Circos
#par(mar = c(1,1,1,1))
par(mar=rep(0,4), cex = 0.7)
chr.names <- paste0("chr", c(1:22, "X", "Y"))
circos.par("start.degree" = 90)
circos.initializeWithIdeogram(species = "hg38",
chromosome.index = chr.names,
plotType = c())
# Legends (center)
legend(-0.5, 0.85, lty = 1, lwd = 2, seg.len = 1, col = c("Red", "Blue"),
legend = c("Deletion", "Insertion"), bty = 'n',
#xjust = 0,
x.intersp = 0.4,
y.intersp = 1,
inset = c(0.05, 0),
title.adj = 0.1,
title = "Small Indels")
legend(-0.5, 0.45, pch = 16, col = cols,
legend = names(cols), pt.cex = 1.2, bty = 'n',
ncol = 3,
#xjust = 0,
x.intersp = 0.4,
y.intersp = 1,
inset = c(0.05, 0),
border = "NA",
title.adj = 0.1,
title = "Substitution")
if ("snpEff_Impact" %in% colnames(tt.subs)) {
legend(-0.5, -0.2, pch = c(10, 13), col = "red",
legend = c("Relevant Impact", "Target Gene"), pt.cex = 1.8, bty = 'n',
ncol = 1,
#xjust = 0,
x.intersp = 0.6,
y.intersp = 1,
inset = c(0.2, 0),
border = "NA",
title.adj = 0,
title = "Highlight Variants")
}
circos.clear()
}
plot_circos <- function(t, sample) {
# Substitutions
t <- mutate(t, TYPE = case_when(nchar(REF) == 1 & nchar(ALT) == 1 & ALT != "*" ~ "Substitution",
nchar(REF) == 1 & nchar(ALT) == 1 & ALT == "*" ~ "Deletion",
nchar(REF) > nchar(ALT) & nchar(REF) - nchar(ALT) >= 1 ~ "Deletion",
nchar(REF) < nchar(ALT) & nchar(ALT) - nchar(REF) >= 1 ~ "Insertion",
TRUE ~ "Other"))
t.subs <- t %>% filter(TYPE == "Substitution") %>% tbl_df()
t.subs$SUB <- paste0(t.subs$REF, t.subs$ALT)
t.subs$SUB <- factor(t.subs$SUB, levels = sort(unique(t.subs$SUB)))
cols_names <- levels(t.subs$SUB)
cols <- snv_colors2(del = F, alpha = 0.8)
t.subs <- merge(t.subs, data.frame(cols, SUB = names(cols)), all.x = TRUE)
tt.subs <- t.subs %>% filter(Sample == sample) %>% tbl_df()
# Deletions / Insertions
t.other <- t %>% filter(TYPE != "Substitution") %>% tbl_df()
tt.other <- t.other %>% filter(Sample == sample) %>% tbl_df()
if(nrow(tt.other) > 0) {
tt.other$VAF <- 1
}
tt.other$col <- ifelse(tt.other$TYPE == "Deletion", "Red", "Blue")
if ("IMPACT" %in% colnames(tt.other)) {
hio <- subset(tt.other, IMPACT %in% c("HIGH"))
if (nrow(hio) > 0) {
print(hio)
}
}
# Circos
par(mar=rep(0,4), cex = 0.9)
chr.names <- paste0("chr", c(1:22, "X", "Y"))
circos.par("start.degree" = 90)
circos.par("track.height" = 0.3)
circos.initializeWithIdeogram(species = "hg38",
chromosome.index = chr.names,
plotType = c("ideogram", "labels"))
lines <- seq(0, 1, 0.2)
line_factors <- expand.grid(x = get.all.sector.index(), y = lines)
circos.trackPlotRegion(factors = line_factors$x,
y = line_factors$y,
track.height = 0.4,
panel.fun = function(x,y) {
xl <- get.cell.meta.data("xlim")
for(i in lines) {
circos.lines(xl, c(i,i), col = "grey")
}
})
for(i in lines) {
circos.text(0, i, i, col = "black", sector.index = "chrY", track.index = 3,
cex = 0.5, adj = c(0.01, 0.01))
}
circos.trackPoints(tt.subs$CHROM,
tt.subs$POS,
tt.subs$VAF,
pch = 16,
cex = 0.9,
col = as.character(tt.subs$cols))
if ("snpEff_Impact" %in% colnames(tt.subs)) {
hi <- subset(tt.subs, snpEff_Impact %in% c("HIGH"))
if (nrow(hi) > 0) {
circos.trackPoints(hi$CHROM,
hi$POS,
hi$VAF,
pch = 10,
cex = 1.2,
col = "red",
track.index = 3)
}
aavs_df <- data.frame("CHROM" = c("chr19"), "POS" = c(44715702), "VAF" = c(0.7))
circos.trackPoints(aavs_df$CHROM,
aavs_df$POS,
aavs_df$VAF,
pch = 13,
cex = 1.5,
col = "red",
track.index = 3)
}
circos.trackPlotRegion(factors = line_factors$x,
ylim = c(0, 1), track.height = 0.1)
if(nrow(tt.other) > 0) {
circos.trackLines(tt.other$CHROM,
tt.other$POS,
tt.other$VAF,
track.index = 4,
col = as.character(tt.other$col),
lwd = 2,
type = 'h')
}
text(0,0, sample, cex = 1.5)
circos.clear()
}
#Define Samples name for analysis
# Samples list to include in analysis
samples <- c("TIP02_A1","TIP02_A3","TIP02_A5","TIP02_AV",
"TIP02_B10","TIP02_B8","TIP02_B9","TIP02_BV",
"TIP02_C16","TIP02_C18","TIP02_CV",
"TIP02_E29","TIP02_E32","TIP02_E33","TIP02_EV",
"A2", "A4", "A6", "AV-96h-1", "B12", "B7", "B-Dx", "BV-96h-2",
"C14", "C15", "C19", "CV-96h-3", "E30", "EV-96h-5")
#Samples order for Circos plot
samples_order_circos <- c("A1","A2","A3","A4","A5","A6",
"B7","B8","B9","B10","B-Dx","B12",
"C14","C15","C16","C18","C19","",
"E29","E30","E32","E33", "", "")
############################################## Analysis #################################################################
#Directory containing ".vcf.gz" files, if vcf file are available, if not skip to line 348-349 and directly read full.t.nomulti.noGerm
wdir <- "VCF_DIR"
#Create dataframe with variants
full.t <- data.frame()
for (ss in samples) {
orig.name = ss
t <- read.table(gzfile(paste(wdir, ss, paste0(ss, "-DP500_PASS_sGQ80_sDP50.ANNOT.dbSNP.vcf.gz"), sep = "/")), comment.char = "#", sep = "\t")
colnames(t) <- c("CHROM","POS","ID","REF","ALT","QUAL","FILTER", "ANNOT", "FORMAT", "SAMPLE")
if (grepl("_", ss)){
ss = unlist(strsplit(ss, "_"))[2]
}
t$Sample <- ss
t$Vars <- paste(t$CHROM, t$POS, t$REF, t$ALT, sep = "-")
t$Group <- substr(ss, 1, 1)
t$Type <- ifelse( substr(ss, 2, 2) == "V", "Vitro", "Sample")
t$DP <- as.numeric(str_split_fixed(t$SAMPLE, ":", 5)[,3])
t$AD <- as.numeric(str_split_fixed(str_split_fixed(t$SAMPLE, ":", 5)[,2], ",", 2)[,1])
t$VAF <- (t$DP - t$AD) / t$DP
t$orig.name = orig.name
t$Common = unlist(lapply(t$ANNOT, function(x) grepl("COMMON", x)))
if (grepl("TIP02", orig.name)){
t$Replica = "A"
}
else{
t$Replica = "B"
}
full.t <- rbind(full.t, t)
}
# Filter CHR only
chr <- c(paste0("chr", seq(1,22)), "chrX")
full.t <- subset(full.t, CHROM %in% chr)
full.t$CHROM <- factor(full.t$CHROM, levels = chr)
# Remove Multi
full.t.nomulti <- full.t[grep(",", full.t$ALT, value = F, invert = T), ]
# Select germline variants from "Vitro" samples
vs_counts <- subset(full.t.nomulti, Type == "Vitro") %>% group_by(Vars) %>% summarise(Count = n())
germ_vars <- subset(vs_counts, Count >= 4)
#Remove Germline vraints shared by vitro samples
full.t.nomulti.noGerm <- subset(full.t.nomulti, !(Vars %in% germ_vars$Vars) )
plot_variants(full.t = full.t.nomulti.noGerm, out_dir = out_dir, plot_prefix = "Full_NoMulti_noGerm", fill_by = "Group")
## Parse snpEff Annotation
full.t.nomulti.noGerm$snpEff_tmp <- lapply(full.t.nomulti.noGerm$ANNOT, function(x){
tmp <- strsplit(x, ";")[[1]]
ann_pos <- length(tmp)
if (!startsWith(tmp[length(tmp)], "ANN")) {
for (i in seq(1,length(tmp))) {
if (startsWith(tmp[i], "ANN")) {
ann_pos <- i
}
}
}
tmp <- tmp[ann_pos]
})
full.t.nomulti.noGerm$snpEff_Effect <- sapply(full.t.nomulti.noGerm$snpEff_tmp, function(tmp){
strsplit(tmp, "|", fixed = T)[[1]][2]
}, USE.NAMES = F)
full.t.nomulti.noGerm$snpEff_Impact <- sapply(full.t.nomulti.noGerm$snpEff_tmp, function(tmp){
strsplit(tmp, "|", fixed = T)[[1]][3]
}, USE.NAMES = F)
full.t.nomulti.noGerm$snpEff_Gene <- sapply(full.t.nomulti.noGerm$snpEff_tmp, function(tmp){
strsplit(tmp, "|", fixed = T)[[1]][4]
}, USE.NAMES = F)
full.t.nomulti.noGerm$snpEff_GeneName <- sapply(full.t.nomulti.noGerm$snpEff_tmp, function(tmp){
strsplit(tmp, "|", fixed = T)[[1]][5]
}, USE.NAMES = F)
full.t.nomulti.noGerm$snpEff_BioType <- sapply(full.t.nomulti.noGerm$snpEff_tmp, function(tmp){
strsplit(tmp, "|", fixed = T)[[1]][8]
}, USE.NAMES = F)
full.t.nomulti.noGerm$snpEff_tmp <- NULL
#Parse SnpEff annotation
snpeff_df <- full.t.nomulti.noGerm %>% group_by(Sample, Group, Type, snpEff_Effect, snpEff_Impact) %>% summarise(Count = n())
snpeff_df <- melt(reshape2::dcast(snpeff_df, Sample + Group + Type ~ snpEff_Effect + snpEff_Impact, value.var = "Count"), id.vars = c("Sample", "Group", "Type"))
snpeff_df$snpEff_Effect <- sapply(snpeff_df$variable, function(x){
tmp <- strsplit(as.character(x), "_")[[1]] # only consider the nearest match
tmp <- tmp[1:(length(tmp)-1)]
paste(tmp, collapse = "_")
}, USE.NAMES = F)
snpeff_df$snpEff_Impact <- sapply(snpeff_df$variable, function(x){
tmp <- strsplit(as.character(x), "_")[[1]] # only consider the nearest match
tmp <- tmp[length(tmp)]
}, USE.NAMES = F)
snpeff_df$variable <- NULL
snpeff_df$snpEff_Impact <- factor(snpeff_df$snpEff_Impact, levels = c("HIGH", "MODERATE", "LOW", "MODIFIER"))
#Mantain only "Sample", remove Vitro samples
samples.t.nomulti.noGerm <- subset(full.t.nomulti.noGerm, Type == "Sample")
#Circos plots for Variants in ClonalHema and CancerRelated Genes
#Directory containing gene list .txt files
glist_dir <- "genePanels/"
#Create dataframe for circos
full.circos <- data.frame()
for (fl in c("CancerRelated.txt", "ClonalHema.txt")) {
offt <- read.table(paste(glist_dir, fl, sep = "/"), header = F, sep = "\t")
colnames(offt) <- c("gene")
lname <- gsub(".txt", "", fl)
print(lname)
offt_res <- list()
offt_res_circos <- list()
for (ss in unique(samples.t.nomulti.noGerm$Sample)) {
samples_filt <- subset(samples.t.nomulti.noGerm, Sample == ss)
offt_var_cond <- subset(samples_filt, snpEff_GeneName %in% offt$gene)
if (nrow(offt_var_cond) > 0) {
full.t.filt <- offt_var_cond %>% filter(grepl("chr", CHROM)) %>% filter(!CHROM %in% c("chrM"))
full.t.filt <- mutate(full.t.filt, TYPE = case_when(nchar(REF) == 1 & nchar(ALT) == 1 & ALT != "*" ~ "Substitution",
nchar(REF) == 1 & nchar(ALT) == 1 & ALT == "*" ~ "Deletion",
nchar(REF) > nchar(ALT) & nchar(REF) - nchar(ALT) >= 1 ~ "Deletion",
nchar(REF) < nchar(ALT) & nchar(ALT) - nchar(REF) >= 1 ~ "Insertion",
TRUE ~ "Other"))
offt_res_circos[[ss]] <- full.t.filt
offt_res[[ss]] <- offt_var_cond
full.t.filt$GL <- lname
full.circos <- rbind(full.circos, full.t.filt)
}
}
}
#Or directly Read dataframe for circos plot
full.circos = read.table("Variants/Full_circos.txt", header = T)
glist_dir <- "genePanels/"
#Plot Circos
for (gl in c("CancerRelated", "ClonalHema")) {
full.circos.gl <- subset(full.circos, GL == gl)
#Per sample
par(mfrow=c(4,6))
samples_order_circos
unique(full.circos.gl$Sample)
for (ss in samples_order_circos ) {
if (ss == ""){
plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
}
else{
plot_circos(full.circos.gl, ss)
}
}
legend_plot(full.circos.gl, ss)
#per Group
full.circos.gl$Sample <- full.circos.gl$Group
par(mfrow=c(2,3))
for (ss in unique(full.circos.gl$Sample)) {
if (ss == ""){
plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
}
else{
plot_circos(full.circos.gl, ss)
}
legend_plot(full.circos.gl, ss)
dev.off()
}
}
#Count number of variant per groups, or direclty read the file in line 399-400
full.t.nomulti.noGerm <- mutate(full.t.nomulti.noGerm,
TYPE = case_when(nchar(REF) == 1 & nchar(ALT) == 1 & ALT != "*" ~ "SNV",
nchar(REF) == 1 & nchar(ALT) == 1 & ALT == "*" ~ "MNV",
nchar(REF) > nchar(ALT) & nchar(REF) - nchar(ALT) >= 1 ~ "DEL",
nchar(REF) < nchar(ALT) & nchar(ALT) - nchar(REF) >= 1 ~ "INS",
TRUE ~ "Other"))
full.t.nomulti.noGerm$Mut.type = paste(full.t.nomulti.noGerm$REF,full.t.nomulti.noGerm$ALT, sep = "")
VarCount = data.frame(table(full.t.nomulti.noGerm[full.t.nomulti.noGerm$Type == "Sample", "Sample"]))
VarCount$Group = substring(VarCount$Var1,1,1)
#Or directly read var Counts
VarCount = read.table("Variants/Variant_counts.txt", header = T)
#Read Callable nucleotides table
callable = read.table("callable_nt/callable_nt.txt", header = T)
#Merge with Variant count dataframe
VarCount = merge(VarCount, callable, by = "Var1")
#Create Sample to Group mapping
replicate = data.frame(t(data.frame(list("A1" = "A",
"A2" = "B",
"A3" = "A",
"A4" = "B",
"A5" = "A",
"A6" = "B",
"B-Dx" = "B",
"B10" = "A",
"B12" = "B",
"B7" = "B",
"B9" = "A",
"B8" = "A",
"C14" = "B",
"C15" = "B",
"C16" = "A",
"C18" = "A",
"C19" = "B",
"E29" = "A",
"E30" = "B",
"E32" = "A",
"E33" = "A",
"F34" = "A",
"F35" = "A",
"F36" = "B",
"F37" = "A",
"F38" = "B"))))
colnames(replicate) = "Replica"
replicate$Sample = gsub("\\.", "-" , rownames(replicate))
#Create dataframe with PCR cycles
pcrcycle = data.frame(t(data.frame(list("A1" = 15,
"A2" = 15,
"A3" = 15,
"A4" = 16,
"A5" = 15,
"A6" = 15,
"B-Dx" = 15,
"B10" = 15,
"B12" = 16,
"B7" = 16,
"B9" = 15,
"B8" = 15,
"C14" = 16,
"C15" = 16,
"C16" = 15,
"C18" = 15,
"C19" = 17,
"E29" = 15,
"E30" = 20,
"E32" = 15,
"E33" = 15,
"F34" = 15,
"F35" = 15,
"F36" = 15,
"F37" = 15,
"F38" = 15))))
colnames(pcrcycle) = "PCRCycle"
pcrcycle$Sample = gsub("\\.", "-" , rownames(pcrcycle))
VarCount = merge(VarCount, replicate, by.x = "Var1", by.y ="Sample", all.x = T)
VarCount = merge(VarCount, pcrcycle, by.x = "Var1", by.y ="Sample", all.x = T)
VarCount$PCRCycle.amp = 2^VarCount$PCRCycle
VarCount$PCRCycle.log = log2(VarCount$PCRCycle)
VarCount$Group = as.factor(VarCount$Group)
#Build lmer Model
model = lmer( Freq ~ PCRCycle.amp + (1 | Group ),
data = VarCount)
#Predict
predicted = data.frame(predict(model, VarCount))
colnames(predicted) = "Predicted"
rownames(predicted) = VarCount$Var1
predicted$Group = VarCount$Group
predicted$Replica = VarCount$Replica
predicted$Sample = rownames(predicted)
predicted = merge(predicted, VarCount, by.x = "Sample", by.y = "Var1")
#Plot number of predicted variants normalized by callable nt
ggplot(predicted, aes(x = Group.x, y = Predicted / Callable)) +
geom_boxplot() +
geom_point()
Variants/Full_circos.txt 0 → 100644
View file @ b0fbe4ac
This diff is collapsed.
Variants/Variant_counts.txt 0 → 100644
View file @ b0fbe4ac
Var1 Freq Group
A1 17059 A
A2 15008 A
A3 17323 A
A4 13246 A
A5 14779 A
A6 16930 A
B-Dx 13201 B
B10 33769 B
B12 7521 B
B7 10245 B
B8 24055 B
B9 29919 B
C14 10878 C
C15 11023 C
C16 14387 C
C18 5407 C
C19 8218 C
E29 21508 E
E30 5064 E
E32 30694 E
E33 19999 E
callable_nt/callable_nt.txt 0 → 100644
View file @ b0fbe4ac
Var1 Threshold Callable
E30 50 70408005
A6 50 82051328
C19 50 80959769
B-Dx 50 83012056
C15 50 82464253
C14 50 83382513
B7 50 82523468
A1 50 90903069
B12 50 83202825
A2 50 83703138
A4 50 83268888
A5 50 93665274
A3 50 96402164
B10 50 98221442
B9 50 96659834
B8 50 94216952
C18 50 81141721
C16 50 87928731
E29 50 94998221
E32 50 95987471
E33 50 93963794
genePanels/CancerRelated.txt 0 → 100644
View file @ b0fbe4ac
ABCB1
ABCC2
ABL1
ABL2
AKT1
AKT2
AKT3
ALK
ANGPTL7
APC
ASXL1
ATM
ATRX
BCYRN1
BRAF
BRCA1
BRCA2
CBL
CDA
CDH1
CDKN2A
CDKN2B
CEBPA
CHD7
CHIC2
CREBBP
CRLF2
CSF1R
CTNNB1
CYP19A1
CYP2A13
CYP2A6
CYP2A7
CYP2B6
CYP2B7P
CYP2C19
CYP2C9
CYP2D6
CYP2D7
DACH1
DDR1
DDR2
DDX3X
DDX54
DNMT3A
DPYD
DPYD-AS1
EGFR
EGFR-AS1
ERBB2
ERBB3
ERBB4
ERG
ESR1
EVI2A
EVI2B
EZH2
FBXW7
FGFR1
FGFR2
FGFR3
FGFR4
FLT1
FLT3
FLT4
FSTL5
GNA11
GNAQ
GNAS
GNAS-AS1
GSTP1
GTF2IP1
H3-3A
HNF1A
HRAS
IDH1
IDH2
IKZF1
IL1RAPL1
IL2RA
IL2RB
IL2RG
INPP4B
JAK1
JAK2
JAK3
KDM6A
KDR
KIT
KMT2A
KRAS
LAMA2
LCK
LOC100287072
LOC101928052
LPAR6
LTK
MAP2K1
MAP2K2
MAP2K4
MAP3K1
MAPK1
MED13
MEIKIN
MET
MGC32805
MGST2
MIR548AZ
MLH1
MPL
MST1R
MTOR
MTOR-AS1
MYC
MYD88
NELL2
NF1
NOTCH1
NPM1
NRAS
OMG
PDGFRA
PDGFRB
PHF6
PIK3CA
PIK3R1
PSMB1
PSMB2
PSMB5
PSMD1
PSMD2
PTCH1
PTEN
PTENP1
PTPN11
PVT1
RAF1
RARA
RARA-AS1
RARB
RARG
RB1
RET
ROS1
RPS6KB1
RUNDC3B
RUNX1
RXRA
RXRB
RXRG
SDCCAG8
SHH
SHOC2
SLC22A1
SLC22A2
SLC31A1
SLC34A2
SLC45A3
SLCO1B1
SMAD4
SMARCA4
SMARCB1
SMO
SNCAIP
SOS1
SPRED1
SRC
STK11
SUFU
TAS2R38
TET2
TMEM75
TMPRSS2
TP53
TPX2
TRRAP
TYK2
UGT1A9
UTY
VHL
WT1
YES1
genePanels/ClonalHema.txt 0 → 100644
View file @ b0fbe4ac
DNMT3A
JAK2
TET2
ASXL1
JAK2
SRSF2
SF3B1
IDH2
SRSF2
TP53
NRAS
U2AF1
NR4A2
RUNX1
FLK2
PDK4
VASN
PRDM16
CDKN1A
PBX1
GATA3
MEF2C
TERT
SH2B3
CHEK2
ATM
PDGFD
PINT
ABCG1
ABCA1
ABCG4
ABCB6
NR4A1
SFPI1
IKAROS
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