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4. The investigation of underlying intrinsic immune landscapes.R
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4. The investigation of underlying intrinsic immune landscapes.R
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# 4.1 Immunogenomic indicator analysis
options(stringsAsFactors=FALSE)
library(ggplot2)
library(ggpubr)
library(ggsci)
clindf <- read.table("risk2.txt", head=TRUE, sep="\t", quote="")
dim(clindf)
clindf[1:6,1:5]
clindf[, "group"] <- factor(clindf[, "risk"])
clindf <- clindf[order(clindf[, "id"]),]
clindf[, "sample_id"] <- substr(clindf[, "id"], 1, 12)
clindf[, "sample_id2"] <- clindf[, "id"]
dim(clindf)
head(clindf)
colnames(clindf)
sum(duplicated(clindf[, "sample_id"]))
clindf <- clindf[!duplicated(clindf[, "sample_id"]),]
sum(duplicated(clindf[, "sample_id"]))
expdf <- read.csv(
"The Immune Landscape of Cancer-mmc2.csv", head=TRUE, quote=""
)
dim(expdf)
expdf[1:6, 1:6]
expdf[, "sample_id"] <- expdf[, "TCGA.Participant.Barcode"]
sum(duplicated(clindf[, "sample_id"]))
sum(duplicated(expdf[, "sample_id"]))
sample_ids <- intersect(clindf[, "sample_id"], expdf[, "sample_id"])
length(sample_ids)
sample_ids[1:10]
clindf2 <- merge(clindf, expdf, by="sample_id", all=FALSE)
dim(clindf2)
colnames(clindf2)
write.csv(clindf2, "clindf2.csv", row.names=FALSE, na="")
newdf1 <- clindf2
dim(newdf1)
ncol(newdf1)
vars <- colnames(newdf1)[10:ncol(newdf1)]
vars
outlst <- list()
for (i in 1:length(vars)) {
print(paste0("------- ", vars[i], " --------"))
newdf2 <- data.frame(
value=newdf1[, vars[i]],
group=newdf1[, "group"]
)
newdf2 <- newdf2[complete.cases(newdf2),]
n <- nrow(newdf2)
model <- wilcox.test(value ~ factor(group), newdf2)
print(model)
means <- aggregate(value ~ factor(group), newdf2, mean)
medians <- aggregate(value ~ factor(group), newdf2, median)
maxs <- aggregate(value ~ factor(group), newdf2, max)
mins <- aggregate(value ~ factor(group), newdf2, min)
outlst[[i]] <- data.frame(
var=vars[i],
n=n,
mean_high=means[1, "value"],
mean_low=means[2, "value"],
median_high=medians[1, "value"],
median_low=medians[2, "value"],
max_high=maxs[1, "value"],
max_low=maxs[2, "value"],
min_high=mins[1, "value"],
min_low=mins[2, "value"],
pvalue=model[["p.value"]]
)
}
outdf <- do.call(rbind, outlst)
outfile <- "result_mean_2.csv"
write.csv(outdf, outfile, row.names=FALSE, na="")
dir.create("fig")
dir.create("outdata")
windowsFonts()
library(scales)
colors1 <- rainbow(length(vars), s=1, v=1, alpha = 1)
barplot(1:12,col=rainbow(12, s=1, v=1, alpha = 1))
colors1
colors2 <- rainbow(length(vars), s=1, v=0.6, alpha = 1)
barplot(1:12,col=rainbow(12, s=1, v=0.6, alpha = 1))
colors2
maxvalues <- apply(outdf[, c("max_high", "max_low")], 1, max)
minvalues <- apply(outdf[, c("min_high", "min_low")], 1, min)
for (i in 1:length(vars)) {
newdf2 <- data.frame(value=newdf1[, vars[i]], group=newdf1[, "group"])
newdf2 <- newdf2[complete.cases(newdf2),]
newdf2[, "group"] <- factor(
newdf2[, "group"], levels=c("high", "low"), labels=c("INB", "IB")
)
windowsFonts()
colors <- c(colors1[i], colors2[i])
title_x <- vars[i]
tb <- table(newdf2[, "group"])
title_label_x <- paste0(names(tb), " (n=", tb, ")")
comparisons <- list(c("INB", "IB"))
symnum.args <- list(
cutpoints=c(0, 0.0001, 0.001, 0.01, 0.05, 1),
symbols=c("", "", "", "", "")
)
labely <- maxvalues[i] + (maxvalues[i] - minvalues[i]) * 0.15
fig <- ggplot(newdf2, aes(x=group, y=value, color=group)) +
geom_boxplot(
aes(colour=group), notch=FALSE,
outlier.size=0.6, outlier.shape=1, outlier.alpha=0.5,
size=1.2,
fatten=1
) +
scale_color_manual(values=colors) +
scale_x_discrete(title_x, labels=title_label_x) +
scale_y_continuous("Value") +
stat_compare_means(
aes(
label=ifelse(
p < 0.001, "P < 0.001", paste0("P = ", ..p.format..)
)
),
label.x=1.25, label.y=labely, family="sans", size=2.8
) +
stat_compare_means(
comparisons=comparisons,
method="t.test",
symnum.args=symnum.args
) +
theme_bw() +
theme(
axis.title.x=element_text(family="sans", size=8),
axis.title.y=element_blank(),
axis.text.x=element_text(
color="black", angle=30, hjust=1,
family="sans", size=8
),
axis.text.y=element_text(color="black", family="sans", size=8),
axis.line=element_line(),
legend.title=element_blank(),
legend.position="none",
panel.grid=element_blank(),
panel.border=element_rect(color=NA)
)
fig
outfile <- paste0("fig/", "fig_boxplot_", i, "_", vars[i], ".tiff")
tiff(outfile, width=4, height=5, unit="cm", res=350, compression="lzw+p")
print(fig)
dev.off()
outfile <- paste0("fig/", "fig_boxplot_", i, "_", vars[i], ".pdf")
pdf(outfile, width=2/2.54, height=5/2.54)
print(fig)
dev.off()
fig <- ggplot(newdf2, aes(x=group, y=value, group=group)) +
geom_violin(
aes(fill=group), color="grey70"
) +
geom_boxplot(
aes(color=group), notch=FALSE,
outlier.size=0.3, outlier.shape=1, outlier.alpha=0.5,
size=0.3, width=0.2,
fatten=1
) +
geom_boxplot(
color="grey70", notch=FALSE,
outlier.colour=NA,
size=0.3, width=0.2,
fatten=0.9
) +
scale_color_manual(values=colors) +
scale_fill_manual(values=colors) +
scale_x_discrete(title_x, labels=title_label_x) +
scale_y_continuous("Value") +
stat_compare_means(
aes(
label=ifelse(
p < 0.001, "P < 0.001", paste0("P = ", ..p.format..)
)
),
label.x=1.25, label.y=labely, family="sans", size=2.5
) +
stat_compare_means(
comparisons=comparisons,
method="wilcox.test",
symnum.args=symnum.args
) +
theme_bw() +
theme(
axis.title.x=element_text(family="sans", size=7),
axis.title.y=element_blank(),
axis.text.x=element_text(
color="black", angle=30, hjust=1, family="sans", size=7),
axis.text.y=element_text(color="black", family="sans", size=7),
axis.line=element_line(),
legend.title=element_blank(),
legend.position="none",
panel.grid=element_blank(),
panel.border=element_rect(color=NA)
)
fig
outfile <- paste0("fig/", "fig_violin_", i, "_", vars[i], ".tiff")
tiff(outfile, width=4, height=5, unit="cm", res=350, compression="lzw+p")
print(fig)
dev.off()
outfile <- paste0("fig/", "fig_violin_", i, "_", vars[i], ".pdf")
pdf(outfile, width=3/2.54, height=5/2.54)
print(fig)
dev.off()
}
# 4.2 Deciphering mutational signatures in the genome
options(stringsAsFactors=FALSE)
mafdf <- read.table("mc3.v0.2.8.PUBLIC.maf", header=TRUE, sep="\t", quote="")
dim(mafdf)
head(mafdf)
mafdf[1:6,1:6]
head(mafdf[, "Tumor_Sample_Barcode"])
mafdf[, "sample_id"] <- substr(mafdf[, "Tumor_Sample_Barcode"], 1, 16)
head(mafdf[, "sample_id"])
sum(duplicated(mafdf[, "sample_id"]))
silent <- c("Silent", "Intron", "RNA", "3'UTR", "3'Flank", "5'UTR", "5'Flank", "IGR")
keeps <- c(
"Missense_Mutation",
"Frame_Shift_Del",
"Frame_Shift_Ins",
"Nonsense_Mutation",
"Nonstop_Mutation",
"Splice_Site",
"Translation_Start_Site"
)
mafdf <- mafdf[mafdf[, "Variant_Classification"] %in% keeps,]
table(mafdf[, "Variant_Classification"])
sample_ids <- levels(factor(mafdf[, "sample_id"]))
length(sample_ids)
sum(duplicated(sample_ids))
symbols <- levels(factor(mafdf[, "Hugo_Symbol"]))
length(symbols)
mafdf_2 <- mafdf
mafdf_2[, "Hugo_Symbol"] <- factor(mafdf_2[, "Hugo_Symbol"], levels=symbols)
mafdf_2[, "sample_id"] <- factor(mafdf_2[, "sample_id"], levels=sample_ids)
mutdf <- as.data.frame(table(mafdf_2[, "Hugo_Symbol"], mafdf_2[, "sample_id"]))
mutdf[, "mut"] <- ifelse(mutdf[, "Freq"] > 0, 1, 0)
head(mutdf)
genedf <- as.data.frame(
matrix(
mutdf[, "mut"], ncol=length(sample_ids), dimnames=list(symbols, sample_ids)
)
)
class(genedf)
dim(genedf)
genedf[1:6,1:6]
sample_ids2 <- substr(sample_ids, 1, 15)
dup_ids2 <- sample_ids2[duplicated(sample_ids2)]
sample_ids[sample_ids2 %in% dup_ids2]
keep_ids <- sample_ids[!duplicated(sample_ids2)]
genedf <- genedf[, keep_ids]
dim(genedf)
genedf[1:6,1:6]
clindf <- read.table("group.txt", header=TRUE, sep="\t", quote="")
dim(clindf)
head(clindf)
clindf[, "sample_id"] <- clindf[, "Mixture"]
dim(clindf)
clindf[1:6,1:3]
mafdf2 <- mafdf
mafdf2[, "sample_id"] <- substr(mafdf2[, "Tumor_Sample_Barcode"], 1, 16)
mafdf2 <- merge(mafdf2, clindf, by="sample_id", all=FALSE)
mafdf2[, "mutation_type6"] <- paste0(
mafdf2[, "Reference_Allele"], ">", mafdf2[, "Tumor_Seq_Allele2"]
)
mafdf2[1:6, "mutation_type6"]
mafdf2[, "mutation_type6"] <- ifelse(
nchar(mafdf2[, "mutation_type6"]) != 3, "Others", mafdf2[, "mutation_type6"]
)
table(mafdf2[, "mutation_type6"])
mafdf2[, "mutation_type6"] <- ifelse(
grepl("-", mafdf2[, "mutation_type6"]), "Others", mafdf2[, "mutation_type6"]
)
table(mafdf2[, "mutation_type6"])
mafdf2[, "mutation_type6"] <- ifelse(
mafdf2[, "mutation_type6"] %in% c("C>A", "G>T"), "C>A",
ifelse(
mafdf2[, "mutation_type6"] %in% c("C>G", "G>C"), "C>G",
ifelse(
mafdf2[, "mutation_type6"] %in% c("C>T", "G>A"), "C>T",
ifelse(
mafdf2[, "mutation_type6"] %in% c("T>A", "A>T"), "T>A",
ifelse(
mafdf2[, "mutation_type6"] %in% c("T>C", "A>G"), "T>C",
ifelse(mafdf2[, "mutation_type6"] %in% c("T>G", "A>C"), "T>G", NA)
)
)
)
)
)
table(mafdf2[, "mutation_type6"])
save.image("project_1.RData")
load("project_1.RData")
library(ggplot2)
library(grid)
library(gtable)
library(ggsci)
newdf1 <- mafdf2[!is.na(mafdf2[, "mutation_type6"]),]
head(newdf1)
newdf1[, "count"] <- 1
newdf2 <- aggregate(count ~ sample_id + mutation_type6 + risk, newdf1, sum)
newdf3 <- aggregate(count ~ sample_id + risk, newdf1, sum)
colnames(newdf3)[3] <- "total"
newdf4 <- merge(newdf2, newdf3, by=c("sample_id", "risk"), all.x=TRUE)
newdf4[, "percent"] <- newdf4[, "count"] / newdf4[, "total"] * 100
figdf <- newdf4
figdf[, "id"] <- figdf[, "sample_id"]
figdf[, "value"] <- figdf[, "percent"]
figdf[, "group"] <- figdf[, "mutation_type6"]
figdf[, "subgroup"] <- figdf[, "risk"]
sort_group <- c("left", "right", "center")[3]
sort_subgroup <- c("left", "right", "center")[3]
colors_group <- c(
rgb(187,87,198, max=255),
rgb(110,110,207, max=255),
rgb(187,204,73, max=255),
rgb(227,68,90, max=255),
rgb(215,97,137, max=255),
rgb(197,51,115, max=255)
)
colors_group
barplot(1:6,col=colors_group)
colors_subgroup <- pal_jco("default")(2)
colors_subgroup <- c(
rgb(86, 86, 220, max=255),
rgb(224, 77, 224, max=255)
)
barplot(1:2,col=colors_subgroup)
r <- 0.5
title_group <- "Mutation"
title_subgroup <- "Risk"
spacing_subgroup <- unit(0.1 * r, "cm")
width_barplot <- 17 * 2.54 * r
width_group <- 1.5 * 2.54 * r
width_subgroup <- 1.5 * 2.54 * r
width <- width_barplot + width_group + width_subgroup
height <- 10 * 2.54 * r
top_group <- 1 * r
top_subgroup <- 1 * r
if (sort_subgroup == "left") {
tb <- table(figdf[, "subgroup"])
subgroups <- names(tb[order(-tb)])
figdf[, "subgroup"] <- factor(figdf[, "subgroup"], levels=subgroups)
} else if (sort_subgroup == "right") {
tb <- table(figdf[, "subgroup"])
subgroups <- names(tb[order(tb)])
figdf[, "subgroup"] <- factor(figdf[, "subgroup"], levels=subgroups)
} else if (sort_subgroup == "center") {
tb <- table(figdf[, "subgroup"])
subgroups <- names(tb[order(-tb)])
subgroups2 <- c()
for (i in 1:length(subgroups)) {
if (i %% 2 == 1) {
subgroups2 <- c(subgroups[i], subgroups2)
} else {
subgroups2 <- c(subgroups2, subgroups[i])
}
}
figdf[, "subgroup"] <- factor(figdf[, "subgroup"], levels=subgroups2)
}
newdf1 <- aggregate(value ~ group, figdf, mean)
groups <- newdf1[order(newdf1[, "value"]), "group"]
figdf[, "group"] <- factor(figdf[, "group"], levels=groups)
if (sort_group == "left") {
figdf <- figdf[
order(figdf[, "subgroup"], figdf[, "group"], -figdf[, "value"]),]
ids <- figdf[figdf[, "group"] == groups[length(groups)], "id"]
figdf[, "id"] <- factor(figdf[, "id"], levels=ids)
} else if (sort_group == "right") {
figdf <- figdf[
order(figdf[, "subgroup"], figdf[, "group"], figdf[, "value"]),]
ids <- figdf[figdf[, "group"] == groups[length(groups)], "id"]
figdf[, "id"] <- factor(figdf[, "id"], levels=ids)
} else if (sort_group == "center") {
figdf <- figdf[
order(figdf[, "subgroup"], figdf[, "group"], -figdf[, "value"]),]
ids <- figdf[figdf[, "group"] == groups[length(groups)], "id"]
ids2 <- c()
for (i in 1:length(ids)) {
if (i %% 2 == 1) {
ids2 <- c(ids[i], ids2)
} else {
ids2 <- c(ids2, ids[i])
}
}
figdf[, "id"] <- factor(figdf[, "id"], levels=ids2)
}
head(figdf)
p <- ggplot(figdf, aes(x=id, y=value, fill=group)) +
geom_bar(position="stack", stat="identity", width=1.1) +
scale_x_discrete("") +
scale_y_continuous("", expand=c(0, 0), limit=c(0, 100)) +
scale_fill_manual(values=colors_group) +
facet_grid(~ subgroup, scales="free", space="free") +
guides(fill=guide_legend(ncol=1, byrow=TRUE)) +
theme(
axis.title=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.line=element_line(),
legend.title=element_text(),
legend.position="none",
legend.justification=c(0, 1),
legend.key=element_rect(fill="transparent", colour="transparent"),
legend.background=element_rect(fill="transparent", colour="black"),
strip.text.x=element_text(color="transparent"),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_line(color=NA),
panel.grid.minor=element_line(color=NA),
panel.spacing.x=unit(spacing_subgroup, "cm"),
plot.background=element_blank()
)
p1 <- p
p <- ggplot(figdf, aes(x=id, y=value, fill=group)) +
geom_bar(position="stack", stat="identity", width=1) +
scale_fill_manual(values=colors_group) +
guides(fill=guide_legend(title=title_group, ncol=1, byrow=TRUE)) +
theme(
legend.title=element_text(),
legend.position="right",
legend.justification=c(0, 1),
legend.key=element_rect(fill="transparent", colour="transparent"),
legend.background=element_rect(fill="transparent", colour="black")
)
p2 <- p
p <- ggplot(figdf, aes(x=id, y=value, fill=subgroup)) +
geom_bar(position="stack", stat="identity", width=1) +
scale_fill_manual(values=colors_subgroup) +
guides(fill=guide_legend(title=title_subgroup, ncol=1, byrow=TRUE)) +
theme(
legend.title=element_text(),
legend.position="right",
legend.justification=c(0, 1),
legend.key=element_rect(fill="transparent", colour="transparent"),
legend.background=element_rect(fill="transparent", colour="black")
)
p3 <- p
g <- ggplotGrob(p1)
strips <- which(grepl('strip-', g[["layout"]][["name"]]))
for (i in 1:length(strips)) {
j <- which(grepl("rect",
g[["grobs"]][[strips[i]]][["grobs"]][[1]][["childrenOrder"]])
)
g[["grobs"]][[strips[i]]][["grobs"]][[1]][[
"children"]][[j]][["gp"]][["fill"]] <- colors_subgroup[i]
}
g1 <- g
g <- ggplotGrob(p2)
guide <- which(sapply(g[["grobs"]], function(x) x$name) == "guide-box")
g2 <- g[["grobs"]][[guide]]
g2[["heights"]][2] <- unit(top_group, "cm")
g <- ggplotGrob(p3)
guide <- which(sapply(g[["grobs"]], function(x) x$name) == "guide-box")
g3 <- g[["grobs"]][[guide]]
g3[["heights"]][2] <- unit(top_subgroup, "cm")
gt <- gtable(
unit(c(width_barplot, width_group, width_subgroup), c("cm")),
unit(height, "cm"))
gt <- gtable_add_grob(gt, g1, 1, 1)
gt <- gtable_add_grob(gt, g2, 1, 2, 1, 2)
gt <- gtable_add_grob(gt, g3, 1, 3, 1, 3)
outfile <- "fig_barplot.pdf"
pdf(outfile, onefile=TRUE, width=width/2.54, height=height/2.54)
grid.draw(gt)
dev.off()
library(ggplot2)
library(ggpubr)
figdf[, "id"] <- figdf[, "sample_id"]
figdf[, "value"] <- figdf[, "percent"]
figdf[, "group"] <- figdf[, "risk"]
figdf[, "var"] <- figdf[, "mutation_type6"]
newdf2 <- figdf
nrow(newdf2)
vars <- levels(factor(newdf2[, "var"]))
newlst1 <- list()
for (i in 1:length(vars)) {
newdf3 <- newdf2[newdf2[, "var"] == vars[i],]
newdf3[, "group"] <- factor(newdf3[, "group"])
model <- wilcox.test(value ~ group, newdf3)
print(model)
means <- aggregate(value ~ factor(group), newdf3, mean)
medians <- aggregate(value ~ factor(group), newdf3, median)
maxs <- aggregate(value ~ factor(group), newdf3, max)
mins <- aggregate(value ~ factor(group), newdf3, min)
newlst1[[i]] <- data.frame(
var=vars[i],
mean_1=means[means[, 1] == "high", "value"],
mean_2=means[means[, 1] == "low", "value"],
median_1=medians[means[, 1] == "high", "value"],
median_2=medians[means[, 1] == "low", "value"],
max_high=maxs[1, "value"],
max_low=maxs[2, "value"],
min_high=mins[1, "value"],
min_low=mins[2, "value"],
pvalue=model[["p.value"]])
}
newdf4 <- do.call(rbind, newlst1)
write.csv(newdf4, "result_mean_2.csv", row.names=FALSE, na="")
windowsFonts()
library(scales)
colors1 <- rainbow(6, s=1, v=1, alpha=1)
barplot(1:6,col=rainbow(6, s=1, v=1, alpha=1))
colors1
colors2 <- rainbow(6, s=1, v=0.6, alpha=1)
barplot(1:6,col=rainbow(6, s=1, v=0.6, alpha=1))
colors2
maxvalues <- apply(newdf4[, c("max_high", "max_low")], 1, max)
minvalues <- apply(newdf4[, c("min_high", "min_low")], 1, min)
for (i in 1:length(vars)) {
newdf3 <- newdf2[newdf2[, "var"] == vars[i],]
colors <- c(colors1[i], colors2[i])
titlex <- vars[i]
comparisons <- list(c("high", "low"))
symnum.args <- list(
cutpoints=c(0, 0.0001, 0.001, 0.01, 0.05, 1),
symbols=c("", "", "", "", "")
)
labely <- maxvalues[i] *1.15
fig <- ggplot(newdf3, aes(x=group, y=value, group=group)) +
geom_boxplot(
aes(colour=group), notch=FALSE,
outlier.size=0.6, outlier.shape=1, outlier.alpha=0.5,
size=0.8,
fatten=0.8
) +
scale_color_manual(values=colors) +
scale_x_discrete(titlex, labels=c("cold tumor", "hot tumor")) +
scale_y_continuous("Value", breaks=seq(0, 100, 20)) +
stat_compare_means(
aes(
label=ifelse(
p < 0.001, "P < 0.001", paste0("P = ", ..p.format..)
)
),
label.x=1.25, label.y=labely, family="sans", size=2.5
) +
stat_compare_means(
comparisons=comparisons,
method="wilcox.test",
symnum.args=symnum.args
) +
theme_bw() +
theme(
axis.title.x=element_text(family="sans", size=7),
axis.title.y=element_blank(),
axis.text.x=element_text(
color="black", angle=30, hjust=1, family="sans", size=7),
axis.text.y=element_text(color="black", family="sans", size=7),
axis.line=element_line(),
legend.title=element_blank(),
legend.position="none",
panel.grid=element_blank(),
panel.border=element_rect(color=NA)
)
fig
outfile <- paste0("fig_boxplot_", i, "_", ".tiff")
tiff(outfile, width=4, height=5, unit="cm", res=350, compression="lzw+p")
print(fig)
dev.off()
outfile <- paste0("fig_boxplot_", i, "_", ".pdf")
pdf(outfile, width=5/2.54, height=6/2.54)
print(fig)
dev.off()
fig <- ggplot(newdf3, aes(x=group, y=value, group=group)) +
geom_violin(
aes(fill=group)
) +
geom_boxplot(
colour="black", notch=FALSE,
outlier.size=0.3, outlier.shape=1, outlier.alpha=0.5,
size=0.3, width=0.2,
fatten=0.8
) +
scale_fill_manual(values=colors) +
scale_x_discrete(titlex, labels=c("cold tumor", "hot tumor")) +
scale_y_continuous("Value", breaks=seq(0, 100, 20)) +
stat_compare_means(
aes(
label=ifelse(
p < 0.001, "P < 0.001", paste0("P = ", ..p.format..)
)
),
label.x=1.25, label.y=labely, family="sans", size=2.5
) +
stat_compare_means(
comparisons=comparisons,
method="wilcox.test",
symnum.args=symnum.args
) +
theme_bw() +
theme(
axis.title.x=element_text(family="sans", size=7),
axis.title.y=element_blank(),
axis.text.x=element_text(
color="black", angle=30, hjust=1, family="sans", size=7),
axis.text.y=element_text(color="black", family="sans", size=7),
axis.line=element_line(),
legend.title=element_blank(),
legend.position="none",
panel.grid=element_blank(),
panel.border=element_rect(color=NA)
)
fig
outfile <- paste0("fig_violin_", i, "_", ".tiff")
tiff(outfile, width=4, height=5, unit="cm", res=350, compression="lzw+p")
print(fig)
dev.off()
outfile <- paste0("fig_violin_", i, "_", ".pdf")
pdf(outfile, width=5/2.54, height=6/2.54)
print(fig)
dev.off()
}
library(deconstructSigs)
library(BSgenome.Hsapiens.UCSC.hg19)
library(MutationalPatterns)
mut_ref <- mafdf2
mut_ref[1:6, 1:12]
mut_ref[, "Chromosome"] <- paste0("chr", mut_ref[, "Chromosome"])
mut_ref[1:6, 1:12]
sample.mut.ref <- mut_ref
snp_count <- mut.to.sigs.input(
mut.ref=sample.mut.ref,
sample.id="sample_id",
chr="Chromosome",
pos="Start_Position",
ref="Reference_Allele",
alt="Tumor_Seq_Allele2",
bsg=BSgenome.Hsapiens.UCSC.hg19
)
snp_count[1:6, 1:6]
mut_mtx <- t(snp_count)
dim(mut_mtx)
mut_mtx[1:6, 1:6]
plot_96_profile(mut_mtx[, c(1, 7)])
plot_96_profile(mut_mtx[,c(1, 7)], condensed=TRUE)
counts <- snp_count
colnames(counts) <- substr(colnames(counts), 3, 5)
counts <- t(counts)
types <- levels(factor(row.names(counts)))
lst <- list()
for (i in 1:length(types)) {
lst[[i]] <- data.frame(
type=types[i],
freq=sum(counts[row.names(counts) == types[i],])
)
}
df <- do.call(rbind, lst)
df$pct <- round(df$freq / sum(df$freq) * 100, 2)
df$type2 <- c(
"C>A/A>G", "C>G/G>C", "C>T/G>A", "T>A/A>T", "T>C/A>G", "T>G/A>C"
)
df
labels <- paste0(df$type2, "\n(", df$pct, "%)")
colors <- c("#999999","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2")
pie(df$pct, labels=labels, col=colors)
outfile <- "fig_1_1.tiff"
tiff(outfile, width=20, height=20, unit="cm", res=350)
pie(df$pct, labels=labels, col=colors)
dev.off()
library(NMF)
mut_mtx2 <- mut_mtx + 0.0001
dim(mut_mtx2)
save.image("project_4_1.RData")
library(MutationalPatterns)
res = nmf(mut_mtx2, rank=4, method="brunet", nrun=30, seed=123456, .opt='p7')
signatures = NMF::basis(res)
contribution = NMF::coef(res)
reconstructed = signatures %*% contribution
nmf_res <- list(signatures = signatures,
contribution = contribution,
reconstructed = reconstructed)
str(nmf_res)
colnames(nmf_res$signatures) <- paste0("Signature", LETTERS[1:4])
rownames(nmf_res$contribution) <- paste0("Signature", LETTERS[1:4])
cancer_signatures = read.table("signatures_probabilities.txt", sep="\t", header=TRUE)
new_order = match(row.names(mut_mat), cancer_signatures$Somatic.Mutation.Type)
cancer_signatures = cancer_signatures[as.vector(new_order),]
row.names(cancer_signatures) = cancer_signatures$Somatic.Mutation.Type
colnames(cancer_signatures)
cancer_signatures = as.matrix(cancer_signatures[,4:33])
all(rownames(cancer_signatures) == rownames(nmf_res$signatures))
cos_sim_signatures = cos_sim_matrix(nmf_res$signatures, cancer_signatures)
plot_cosine_heatmap(cos_sim_signatures, cluster_rows=FALSE, plot_values=TRUE)
outfile <- "fig_cosine_heatmap.tiff"
tiff(outfile, width=30, height=20, unit="cm", res=350, compression="lzw+p")
plot_cosine_heatmap(cos_sim_signatures, cluster_rows=FALSE, plot_values=TRUE)
dev.off()
outfile <- "fig_cosine_heatmap.pdf"
pdf(outfile, onefile=FALSE, width=30/2.54, height=20/2.54)
plot_cosine_heatmap(cos_sim_signatures, cluster_rows=FALSE, plot_values=TRUE)
dev.off()
save.image("project_4.RData")
save(nmf_res, file="nmf_res_rank4.RData")
options(stringsAsFactors=FALSE)
library(NMF)
load("mut_mat.RData")
load("nmf_res.RData")
load("mut_mtx.RData")
library(MutationalPatterns)
plot_96_profile(nmf_res$signatures)
outfile <- "fig_barplot_5_1_test.tiff"
tiff(outfile, width=20, height=20, unit="cm", res=350, compression="lzw+p")
plot_96_profile(nmf_res$signatures)
dev.off()
mut_matrix <- nmf_res[["signatures"]]
norm_mut_matrix = apply(mut_matrix, 2, function(x) x / sum(x))
CONTEXTS_96 <- rownames(mut_matrix)
context = CONTEXTS_96
SUBSTITUTIONS <- c(
"C>A",
"C>G",
"C>T",
"T>A",
"T>C",
"T>G"
)
substitution = rep(SUBSTITUTIONS, each=16)
context <- paste0(substr(context, 1, 1), ".", substr(context, 7, 7))
df = data.frame(substitution=substitution, context=context)
rownames(norm_mut_matrix) = NULL
library(reshape2)
df2 = cbind(df, as.data.frame(norm_mut_matrix))
df3 = melt(df2, id.vars=c("substitution", "context"))
head(df3)
df4 <- df3
df4[, "value"] <- round(df4[, "value"] * 100, 1)
outdf <- df4
outfile <- "result_signature_96.csv"
write.csv(outdf, outfile, row.names=FALSE, na="")
library(ggplot2)
library(grid)
library(gtable)
library(scales)
colors <- c(
rgb(204,0,255, max=255),
rgb(255,153,0, max=255),
rgb(51,255,0, max=255),
rgb(197,51,115, max=255),
rgb(160,64,255, max=255),
rgb(255,0,153, max=255)
)
colors
colors_subgroup <- c(
rgb(204,0,255, max=255),
rgb(255,153,0, max=255),
rgb(51,255,0, max=255),
rgb(197,51,115, max=255),
rgb(160,64,255, max=255),
rgb(255,0,153, max=255)
)
colors_subgroup
ymax = 0.35
signatures <- c("SignatureA", "SignatureB", "SignatureC", "SignatureD")
for (i in 1:length(signatures)) {
df4 <- df3[df3[, "variable"] == signatures[i],]
df4[, "substitution2"] <- ""
gplot <- ggplot(
data=df4,
aes(
x=context,
y=value,
fill=substitution,
width=0.6),
) +
geom_rect(
aes(fill=substitution),
alpha=0.01, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf
) +
geom_bar(stat="identity", colour="transparent", size=0.2) +
scale_fill_manual(values=colors) +
facet_wrap(
~ substitution2 + substitution, strip.position="bottom", nrow=1
) +
coord_cartesian(ylim=c(0, ymax)) +