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METABRIC_Analysis.R
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library(ggplot2)
library(egg)
library(ggpubr)
library(survminer)
library(ggsci)
### Load results from 50 repeat 10-fold cross-validation analyses ------------
load("./../Output/metabric_er.RData")
load("./../Output/metabric.ssGSEA.RData")
FS_C2 <- readRDS("./../Output/MB_C2_Model_cv_results_FeatureSet.RDS")
FS_hall <- readRDS("./../Output/MB_H_Model_cv_results_FeatureSet.RDS")
FS_sg <- readRDS("./../Output/MB_Cox_cv_results_FeatureSet.RDS")
FS_ENDORSE <- readRDS("./../Output/MB_CoxNoClin_cv_results_FeatureSet.RDS")
FS_ENDORSE2 <- readRDS("./../Output/MB_CoxNoClin_cv_2_results_FeatureSet.RDS")
Res_C2 <- readRDS("./../Output/MB_C2_Model_cv_results_Restable.RDS")
Res_hall <- readRDS("./../Output/MB_H_Model_cv_results_Restable.RDS")
Res_sg <- readRDS("./../Output/MB_Cox_cv_results_Restable.RDS")
Res_ENDORSE <- readRDS("./../Output/MB_CoxNoClin_cv_results_Restable.RDS")
Res_ENDORSE2 <- readRDS("./../Output/MB_CoxNoClin_cv_2_results_Restable.RDS")
FS_sg <- FS_sg[-grep("NA/NaN", FS_sg)]
#### Plot the general characterisitics of the models ------------------------------------
# 1. Features in each analysis (table barplot)
FS <- list(FS_C2, FS_hall, FS_sg, FS_ENDORSE)
Plts <- list(); Plts2 <- list()
for (i in 1:length(FS)) {
df <- data.frame(sort(table(FS[[i]]), decreasing = T))
colnames(df) <- c("Feature", "Frequency")
Plts2[[i]] <-ggplot(data=df, aes(x=Frequency)) +
geom_histogram(color="white", fill="#000370", binwidth = 25) +
geom_vline(xintercept = 250) +
labs(x="Frequency", y="Count") +
theme_classic()
df <- df[which(df$Frequency > 250), ]
print(sum(df$Frequency == 500))
#sz <- max(10 - nrow(df)/50, 3)
Plts[[i]] <- ggplot(data=df) +
geom_bar(aes(x=Feature, y=Frequency), width=0.5, color=NA, fill='#FFC67E', stat='identity') +
labs(x="") +
theme_classic() +
coord_flip()
# write.csv(df, file=paste("./../Output/Feature_Set_Table_", i, ".csv", sep=""))
}
png(file="./../Figures/Feature_bar.png", height=3000, width=3600, res=200)
egg::ggarrange(Plts[[1]], Plts[[2]], Plts[[3]], Plts[[4]], ncol=2)
dev.off()
png(file="./../Figures/Feature_hist.png", height=1600, width=3600, res=200)
egg::ggarrange(Plts2[[1]], Plts2[[2]], Plts2[[3]], Plts2[[4]], ncol=2)
dev.off()
# 2. Consensus geneset application - METABRIC stratification with risk score
ENDORSE <- names(which(table(FS_ENDORSE) > 250)) # Define ENDORSE as genes in atleast 50% of the cross-validations (>250)
saveRDS(ENDORSE, file="./../Output/ENDORSE.RDS")
# Calculate ENDORSE enrichment score
MB.ENDORSE <- gsva(metabric.exp, gset.idx.list = list("ENDORSE"=ENDORSE, "E2"=ENDORSE_NO_PROL),
method="ssgsea", kcdf="Gaussian")
df <- data.frame('Time'=metabric.annotV2$OS_MONTHS,
'Event'=as.numeric(metabric.annotV2$OS_EVENT),
'Age'=metabric.annotV2$AGE_AT_DIAGNOSIS,
'ENDORSE'=MB.ENDORSE[1,],
'ENDORSE_NO_PROL'=MB.ENDORSE[2,])
cfit = coxph(Surv(Time, Event) ~ ENDORSE, data=df)
saveRDS(cfit, file="./../Output/ENDORSE.CoxFit.RDS")
coxph(Surv(Time, Event) ~ ENDORSE_NO_PROL, data=df)
res <- summary(cfit)
# Three steps to predict and classify sample:
# 1. Calculate ENDORSE enrichment score (gsva)
# 2. Apply ENDORSE to predict risk (predict(coxfit)
# 3. Classify risk grp (stratify)
MB.risk <- predict(cfit, type='risk')
MB.risk.grps <- MakeRiskGrp(MB.risk)
MB.Surv.df <- data.frame("Time"=metabric.annotV2$OS_MONTHS,
"Event"=as.numeric(metabric.annotV2$OS_EVENT),
"Age"=metabric.annotV2$AGE_AT_DIAGNOSIS,
"Group"=MB.risk.grps)
fit <- survfit(Surv(Time, Event) ~ Group, data=MB.Surv.df)
Plt <- ggsurvplot(fit, data=MB.Surv.df, palette="nejm",
censor.shape="|", censor.size=2,
break.time.by=60,
legend.title="ENDORSE", legend.labs=c("Low risk", "Medium risk", "High risk"),
surv.median.line = "hv",
risk.table=T,
ggtheme=theme_classic2(base_size = 18))
ggexport(Plt, filename ="./../Figures/METABRIC.Cox.Survival.pdf", width=8, height=9)
dev.off()
table(MB.risk.grps)
hist(MB.risk, freq=T, breaks=10)
lines(ecdf(MB.risk), lwd=2, col="skyblue")
abline(v=c(1,2), lwd=2, lty=2, col="darkred")
100 * sum(MB.risk >= 2) / length(MB.risk)
100 * sum(MB.risk <= 1) / length(MB.risk)
# Make histogram and ECDF of risk distribution
Plt[[1]] <- ggplot(data.frame(MB.risk), aes(MB.risk)) +
geom_histogram(color=NA, fill='#2D004E', bins=50) +
geom_rug(color="#8D8D8D") +
labs(x="ENDORSE risk", y="Frequency") +
#geom_vline(xintercept = c(1,2), color = "grey50", size=0.75) +
scale_x_continuous(breaks=c(1, 2, 3, 4)) +
theme_classic(base_size = 18)
Plt[[2]] <- ggplot(data.frame(MB.risk), aes(MB.risk)) +
stat_ecdf(geom = "step", size=1.5, color="#D10000") +
labs(y = "F(risk)", x="ENDORSE risk") +
geom_hline(yintercept = c(0.4622, 0.8367), linetype = "dotted", color="#858585") +
#geom_segment(x=0, xend=2, y=0.8367, yend = 0.8367, size=0.5, linetype = "dashed", col="#8D8D8D") +
#geom_segment(x=0, xend=1, y=0.4622, yend = 0.4622, size=0.5, linetype = "dashed", col="#8D8D8D") +
geom_segment(x=2, xend=2, y=0, yend = 0.8367,linetype = "dotted", col="#858585") +
geom_segment(x=1, xend=1, y=0, yend = 0.4622, linetype = "dotted", col="#858585") +
scale_x_continuous(breaks=c(1, 2, 3, 4)) +
theme_classic(base_size = 18)
pdf("./../Figures/METABRIC.CoxHistEcdf.pdf", width=8, height=4)
egg::ggarrange(Plt[[1]], Plt[[2]], ncol=2)
dev.off()
# Heatmap of ENDORSE genes across patient samples grouped by risk strat
library(RColorBrewer)
library(ComplexHeatmap)
column_ha = HeatmapAnnotation(#ENDORSE = anno_barplot(MB.ENDORSE[1,], gp = gpar(fill = "blue")),
Risk = anno_barplot(MB.risk, gp = gpar(col = "darkred")))
Heatmap(t(scale(t(metabric.exp[ENDORSE, ]))),
name = "Scaled expresssion", #title of legend
column_title = "METABRIC ER+ Breast Cancers", row_title = "ENDORSE genes",
show_column_names = FALSE,
row_names_gp = gpar(fontsize = 6), # Text size for row names
column_split = MB.risk.grps,
#column_order = sort(MB.risk),
col = brewer.pal(5, "PiYG"),
top_annotation = column_ha)
Heatmap(#t(scale(t(
metabric.hall,#))),
name = "Hallmark GES", #title of legend
column_title = "METABRIC ER+ Breast Cancers", row_title = "ENDORSE genes",
show_column_names = FALSE,
row_names_gp = gpar(fontsize = 6), # Text size for row names
column_split = MB.risk.grps,
#column_order = sort(MB.risk),
col = brewer.pal(5, "PiYG"),
top_annotation = column_ha)
Heatmap(t(scale(t(metabric.exp[c("ESR1", "PGR", "GATA1", "GATA3") , ]))),
name = "Scaled expression", #title of legend
column_title = "METABRIC ER+ Breast Cancers", row_title = "ENDORSE genes",
show_column_names = FALSE,
row_names_gp = gpar(fontsize = 6), # Text size for row names
column_split = MB.risk.grps,
#column_order = sort(MB.risk),
col = brewer.pal(5, "PiYG"),
top_annotation = column_ha)
coxph(Surv(metabric.annotV2$OS_MONTHS, as.numeric(metabric.annotV2$OS_EVENT)) ~
metabric.exp["ESR1", ])
coxph(Surv(metabric.annotV2$OS_MONTHS, as.numeric(metabric.annotV2$OS_EVENT)) ~
metabric.exp["PGR", ])
coxph(Surv(metabric.annotV2$OS_MONTHS, as.numeric(metabric.annotV2$OS_EVENT)) ~
metabric.exp["GATA1", ])
coxph(Surv(metabric.annotV2$OS_MONTHS, as.numeric(metabric.annotV2$OS_EVENT)) ~
metabric.exp["GATA3", ])
# 3. Simulate effects of dropouts ---------------------------------------
# A.Perform analysis with dropouts in signature genes (probably should do a full transcriptome dropout)
MB.DS <- list()
set.seed(1234)
for (i in 1:9) {
print(i)
cut1 <- sample(x = nrow(metabric.exp), size = nrow(metabric.exp) - (0.1*i*nrow(metabric.exp)),
replace = FALSE)
print(length(cut1))
MB.DS[[i]] <- gsva(metabric.exp[cut1, ], gset.idx.list = list("ENDORSE"=ENDORSE),
method="ssgsea", kcdf="Gaussian")
}
# Make the plots
MB.DS.Plts <- list(); MB.DS.res <- list()
for (i in 1:length(MB.DS)) {
df <- data.frame('Time'=metabric.annotV2$OS_MONTHS,
'Event'=as.numeric(metabric.annotV2$OS_EVENT),
'Age'=metabric.annotV2$AGE_AT_DIAGNOSIS,
'ENDORSE'=MB.DS[[i]][1,])
cfit = coxph(Surv(Time, Event) ~ Age + ENDORSE, data=df)
TEMP.risk <- predict(cfit, type='risk')
TEMP.risk.grps <- MakeRiskGrp(TEMP.risk)
TEMP.Surv.df <- data.frame("Time"=metabric.annotV2$OS_MONTHS,
"Event"=as.numeric(metabric.annotV2$OS_EVENT),
"Age"=metabric.annotV2$AGE_AT_DIAGNOSIS,
"Group"=TEMP.risk.grps)
fit <- survfit(Surv(Time, Event) ~ Group, data=TEMP.Surv.df)
MB.DS.Plts[[i]] <- ggsurvplot(fit, data=TEMP.Surv.df, palette="nejm",
censor.shape="|", censor.size=1,
break.time.by=60,
legend="none",
#legend.title="ENDORSE", legend.labs=c("Low risk", "Medium risk", "High risk"),
#surv.median.line = "hv",
risk.table=F,
ggtheme=theme_classic2(base_size = 10))
MB.DS.res[[i]] = coxph(Surv(Time, Event) ~ Age + ENDORSE, data=df)
}
# B. Perform analysis with reduced number of samples
MB.L <- list(); MB.Lsam <- list()
set.seed(1234)
for (i in 1:9) {
print(i)
res <- list()
cut2 <- sample(x = 1:ncol(metabric.exp), size = 0.1*i*ncol(metabric.exp),
replace=FALSE)
MB.L[[i]] <- gsva(metabric.exp[, cut2], gset.idx.list = list("ENDORSE"=ENDORSE),
method="ssgsea", kcdf="Gaussian")
MB.Lsam[[i]] <- cut2
}
# Make the plots
MB.L.Plts <- list(); MB.L.res <- list()
for (i in 1:length(MB.L)) {
MB.Lsam
df <- data.frame('Time'=metabric.annotV2$OS_MONTHS[MB.Lsam[[i]]],
'Event'=as.numeric(metabric.annotV2$OS_EVENT[MB.Lsam[[i]]]),
'Age'=metabric.annotV2$AGE_AT_DIAGNOSIS[MB.Lsam[[i]]],
'ENDORSE'=MB.L[[i]][1,])
cfit = coxph(Surv(Time, Event) ~ Age + ENDORSE, data=df)
TEMP.risk <- predict(cfit, type='risk')
TEMP.risk.grps <- MakeRiskGrp(TEMP.risk)
TEMP.Surv.df <- data.frame("Time"=metabric.annotV2$OS_MONTHS[MB.Lsam[[i]]],
"Event"=as.numeric(metabric.annotV2$OS_EVENT)[MB.Lsam[[i]]],
"Age"=metabric.annotV2$AGE_AT_DIAGNOSIS[MB.Lsam[[i]]],
"Group"=TEMP.risk.grps)
fit <- survfit(Surv(Time, Event) ~ Group, data=TEMP.Surv.df)
MB.L.Plts[[i]] <- ggsurvplot(fit, data=TEMP.Surv.df, palette="nejm",
censor.shape="|", censor.size=1,
break.time.by=60,
legend="none",
#legend.title="ENDORSE", legend.labs=c("Low risk", "Medium risk", "High risk"),
#surv.median.line = "hv",
risk.table=F,
ggtheme=theme_classic2(base_size = 10))
MB.L.res[[i]] = coxph(Surv(Time, Event) ~ Age + ENDORSE, data=df)
}
pdf(file="./../Figures/MB_Simulations.pdf", width=7, height=6)
arrange_ggsurvplots(MB.DS.Plts, nrow=3, ncol=3)
arrange_ggsurvplots(rev(MB.L.Plts), nrow=3, ncol=3)
dev.off()
### 4. Table of Cox proportional hazards model ------------------------------
#Clinical vars
df <- data.frame('Time'=metabric.annotV2$OS_MONTHS,
'Event'=as.numeric(metabric.annotV2$OS_EVENT),
'Age'=metabric.annotV2$AGE_AT_DIAGNOSIS,
'TumorSize'=MB.annot.extraV2$Tumor.Size,
'TumorStage'=MB.annot.extraV2$Tumor.Stage,
'MutationCount'=MB.annot.extraV2$Mutation.Count,
'LymphNode'=MB.annot.extraV2$Lymph.nodes.examined.positive)
METABRIC.Clinical.cfit <- coxph(Surv(time = Time, event = Event) ~ ., data = df)
getMeanCI <- function(x) {
temp <- t.test(x, na.rm=T)
res <- data.frame("Mean"=signif(mean(x, na.rm=T), 3),
"N"=sum(!is.na(x)),
"CI"=paste(signif(temp$conf.int[1], 3), signif(temp$conf.int[2], 3), sep=" - "))
return(res)
}
apply(df, 2, getMeanCI)
#Other classifiers
df <- data.frame('Time'=metabric.annotV2$OS_MONTHS,
'Event'=as.numeric(metabric.annotV2$OS_EVENT),
'Age'=metabric.annotV2$AGE_AT_DIAGNOSIS,
'TumorSize'=MB.annot.extraV2$Tumor.Size,
'TumorStage'=MB.annot.extraV2$Tumor.Stage,
'MutationCount'=MB.annot.extraV2$Mutation.Count,
'LymphNode'=MB.annot.extraV2$Lymph.nodes.examined.positive,
'PAM50'=MB.annot.extraV2$Pam50...Claudin.low.subtype,
'IntClust'=MB.annot.extraV2$Integrative.Cluster,
'ESR'=metabric.exp["ESR1",],
'PGR'=metabric.exp["PGR",],
'ENDORSE'=MBsc.ssgsea[1,],
'RiskGroups'=MB.risk.grps)
METABRIC.PAM.cfit <- coxph(Surv(time = Time, event = Event) ~ PAM50, data = df)
METABRIC.IC.cfit <- coxph(Surv(time = Time, event = Event) ~ IntClust, data = df)
METABRIC.Recptors.cfit <- coxph(Surv(time = Time, event = Event) ~ ESR + PGR, data = df)
METABRIC.ENDORSE.cfit <- coxph(Surv(time = Time, event = Event) ~ ENDORSE, data = df)
METABRIC.RG.cfit <- coxph(Surv(time = Time, event = Event) ~ RiskGroups, data = df)
CoxExport <- function(cfit, fname) {
temp <- summary(cfit)
df1 <- data.frame(temp$coefficients)
df2 <- data.frame(temp$conf.int)
c.table <- data.frame("Variable"=rownames(df1),
"HR"=signif(df1$exp.coef., 3),
"95%CI"=paste(signif(df2$lower..95, 3), signif(df2$upper..95, 3), sep=" - "),
"P"=signif(df1$Pr...z.., 2))
write.csv(c.table, file=fname)
}
CoxExport(METABRIC.PAM.cfit, fname="./../Output/METABRIC.PAM.cfit.csv")
CoxExport(METABRIC.IC.cfit, fname="./../Output/METABRIC.IC.cfit.csv")
CoxExport(METABRIC.Recptors.cfit, fname="./../Output/METABRIC.Recptors.cfit.csv")
CoxExport(METABRIC.ENDORSE.cfit, fname="./../Output/METABRIC.ENDORSE.cfit.csv")
CoxExport(METABRIC.RG.cfit, fname="./../Output/METABRIC.RG.cfit.csv")
CoxExport(METABRIC.Clinical.cfit, fname="./../Output/METABRIC.Clinical.cfit.csv")
# Multivariate analyses
CoxExport(coxph(Surv(time = Time, event = Event) ~ PAM50 + RiskGroups, data = df),
fname = "./../Output/Multi_ENDORSE_PAM50.cfit.csv")
CoxExport(coxph(Surv(time = Time, event = Event) ~ IntClust + RiskGroups, data = df),
fname = "./../Output/Multi_ENDORSE_IntClust.cfit.csv")
CoxExport(coxph(Surv(time = Time, event = Event) ~ ESR + PGR + RiskGroups, data = df),
fname = "./../Output/Multi_ENDORSE_ESRPGR.cfit.csv")
#### Negative control: METABRIC ER- ----------------------------------
x <- match(colnames(metabric), metabric.annot$PATIENT_ID)
metabric_N.annot <- metabric.annot[x, ]
OS_EVENT <- array(dim=nrow(metabric_N.annot))
OS_EVENT[which(metabric_N.annot$OS_STATUS == "LIVING")] <- 0
OS_EVENT[which(metabric_N.annot$OS_STATUS == "DECEASED")] <- 1
metabric_N.annot$OS_EVENT <- OS_EVENT
y <- which(metabric_N.annot$ER_IHC == "Negative")
metabric_N.annot <- metabric_N.annot[y,]
metabric.N <- metabric[, y]
metabric.N <- scale(t(metabric.N))
ENDORSE <- readRDS("./../Output/ENDORSE.RDS")
MB_N.ssgsea <- gsva(t(metabric.N), list("ENDORSE"=ENDORSE),
verbose=TRUE, kcdf="Gaussian", method='ssgsea', ssgsea.norm=T)
MB_N.ssgsea[1,] <- ReScale(MB_N.ssgsea[1,])
# Predict risk groups
# Train df
df <- data.frame('Time'=metabric.annotV2$OS_MONTHS,
'Event'=as.numeric(metabric.annotV2$OS_EVENT),
'Age'=metabric.annotV2$AGE_AT_DIAGNOSIS,
'ENDORSE'=MBsc.ssgsea[1,])
cfit = coxph(Surv(Time, Event) ~ ENDORSE, data=df)
# predict
MB_N.pred <- predict(cfit, data.frame("ENDORSE"=MB_N.ssgsea[1,]), type="risk")
# compare
# plot - by risk groups
MB_N.pred.grp <- MakeRiskGrp(MB_N.pred, thresh=c(1,2))
df <- data.frame("Time"=as.numeric(metabric_N.annot$OS_MONTHS),
"Event"=metabric_N.annot$OS_EVENT,
"RiskGroup"=MB_N.pred.grp,
"Risk"=MB_N.pred,
"ENDORSE"=MB_N.ssgsea[1,])
# Survival plots ----
library(survminer)
fit.os <- survfit(Surv(Time, Event) ~ RiskGroup, data=df)
surv_median(fit.os)
P1 <- ggsurvplot(fit.os, data=df, palette="nejm",
censor.shape="|", censor.size=2,
break.time.by=60,
legend.title="ENDORSE", legend.labs=c("Low risk", "Medium risk", "High risk"),
risk.table=F,
title="ER- METABRIC breast cancers",
ggtheme=theme_classic2(base_size = 18))
summary(coxph(Surv(Time, Event) ~ RiskGroup, data=df))
pdf("./../Figures/METABRIC_ER_Neg.pdf", width=6, height=6)
print(P1)
dev.off()
### PATHWAYS and MUTATIONS in RISK GROUPS ------------------------------
############ Curated and Hallmark pathways ### ----------------------------------------------
y <- match(colnames(metabric.s), colnames(metabric.c2))
metabric.c2 <- metabric.c2[,y]
y <- match(colnames(metabric.s), colnames(metabric.c2))
metabric.hall <- metabric.hall[,y]
## Continuous test
CorFilter <- function(Xmat, Y, thresh=0.5, method='spearman') {
Cor.tests <- apply(Xmat, 2, cor.test, Y, method=method)
Cor.tests.p <- p.adjust(sapply(Cor.tests, "[", 3), method = 'fdr')
Cor.tests.r <- as.numeric(sapply(Cor.tests, "[", 4))
Filt <- which(Cor.tests.p < 0.05 & abs(Cor.tests.r) > thresh)
return(data.frame("Name"=colnames(Xmat)[Filt], "p"=Cor.tests.p[Filt], "r"=Cor.tests.r[Filt]))
}
MB.hall.cors <- CorFilter(t(metabric.hall), E2.risk, thresh = 0.5)
MB.c2.cors <- CorFilter(t(metabric.c2), E2.risk, thresh = 0.5)
MB.c2.cors[grep("SIGNALING", MB.c2.cors$Name), ]
MB.c2all.cors <- CorFilter(t(metabric.c2), E2.risk, thresh = 0.3)
MB.c2all.cors[grep("SIGNALING", MB.c2all.cors$Name), ]
## Binary test
MBRisk.hall.p <- apply(metabric.hall, 1, function (x) cor.test(x[I_lm], x[I_h])$p.value)
MBRisk.c2.p <- apply(metabric.c2, 1, function (x) t.test(x[I_lm], x[I_h])$p.value)
MBRisk.hall.e <- apply(metabric.hall, 1, function (x) {
(mean(x[I_h]) - mean(x[I_lm]))/sd(x)
})
MBRisk.c2.e <- apply(metabric.c2, 1, function (x) {
(mean(x[I_h]) - mean(x[I_lm]))/sd(x)
})
write.csv(data.frame("Effect"=MBRisk.hall.e,
"P-value"=MBRisk.hall.p,
"FDR"=p.adjust(MBRisk.hall.p)),
file="./../Output/MBRisk.hall_v2.csv")
write.csv(data.frame("Effect"=MBRisk.c2.e,
"P-value"=MBRisk.c2.p,
"FDR"=p.adjust(MBRisk.c2.p)),
file="./../Output/MBRisk.c2_v2.csv")
# Important pathways
HRC2Dn <- names(which(MBRisk.c2.e < -0.8 & p.adjust(MBRisk.c2.p) < 0.05)) #12
HRC2Up <- names(which(MBRisk.c2.e > 0.8 & p.adjust(MBRisk.c2.p) < 0.05)) #229
write.csv(MBRisk.c2.p[HRC2Up], "./../Output/HRC2Up.csv")
write.csv(MBRisk.c2.p[HRC2Dn], "./../Output/HRC2Dn.csv")
## Heatmaps of some pathways based on common themes
library(RColorBrewer)
library(stringr)
coul <- colorRampPalette(c("navy", "white", "darkred"))(15)
pdf("./../Figures/MBRisk_Pathway_Heatmaps.pdf", height=8, width=12)
heatmap(metabric.c2[grep("P53", HRC2Up, value=T), c(I_lm, I_h)],
scale = "row",
col = coul,
cexRow = 1.25,
Colv = NA,
margins = c(25,25),
ColSideColors = c(rep("darkred", length(I_lm)), rep("salmon", length(I_h))),
labCol= NA,
labRow = str_wrap(gsub("_"," ", grep("P53", HRC2Up, value=T)), width=50))
l1 <- grep("RB", HRC2Up, value=T)
l1 <- l1[-2]
heatmap(metabric.c2[l1, c(I_lm, I_h)],
scale = "row",
col = coul,
cexRow = 1.25,
Colv = NA,
margins = c(35,25),
ColSideColors = c(rep("darkred", length(I_lm)), rep("salmon", length(I_h))),
labCol= NA,
labRow = str_wrap(gsub("_"," ", l1), width=50))
l1 <- grep("CELL_CYCLE", HRC2Up, value=T)
l1 <- l1[-c(5,6,15,16,17)]
heatmap(metabric.c2[l1, c(I_lm, I_h)],
scale = "row",
col = coul,
cexRow = 1.25,
Colv = NA,
margins = c(25,25),
ColSideColors = c(rep("darkred", length(I_lm)), rep("salmon", length(I_h))),
labCol= NA,
labRow = str_wrap(gsub("_"," ", l1), width=50))
l1 <- c(grep("RECOMBINATION", HRC2Up, value=T),
grep("REPAIR", HRC2Up, value=T),
grep("ATR", HRC2Up, value=T),
grep("ATM", HRC2Up, value=T))
l1 <- l1[-11]
heatmap(metabric.c2[l1, c(I_lm, I_h)],
scale = "row",
col = coul,
cexRow = 1.25,
Colv = NA,
margins = c(25,25),
ColSideColors = c(rep("darkred", length(I_lm)), rep("salmon", length(I_h))),
labCol= NA,
labRow = str_wrap(gsub("_"," ", l1), width=50))
heatmap(metabric.c2[HRC2Up, c(I_lm, I_h)],
scale = "row",
col = coul,
cexRow = 0.25,
Colv = NA,
margins = c(5,25),
ColSideColors = c(rep("darkred", length(I_lm)), rep("salmon", length(I_h))),
labCol= NA,
labRow = NA)
#labRow = str_wrap(gsub("_"," ", HRC2Up), width=150))
legend_image <- as.raster(matrix(coul, nrow=1))
plot.new()
rasterImage(legend_image, 0, 0.5, 1, 1)
text(y=0.35, x= seq(0, 1, l=5), cex=2,
labels = round(quantile(scale(t(metabric.c2[HRC2Up, c(I_lm, I_h)]))), 1))
heatmap(metabric.c2[HRC2Dn, c(I_lm, I_h)],
scale = "row",
col = coul,
cexRow = 1.25,
Colv = NA,
margins = c(25,25),
ColSideColors = c(rep("darkred", length(I_lm)), rep("salmon", length(I_h))),
labCol= NA,
labRow = str_wrap(gsub("_"," ", HRC2Dn), width=50))
legend_image <- as.raster(matrix(coul, nrow=1))
plot.new()
rasterImage(legend_image, 0, 0.5, 1, 1)
text(y=0.35, x= seq(0, 1, l=5), cex=2,
labels = round(quantile(scale(t(metabric.c2[HRC2Dn, c(I_lm, I_h)]))), 1))
dev.off()
#### Make individual plots
library(stringr)
risk.labs <- array(dim=length(MB.risk.grps))
risk.labs[I_lm] <- "Low/Med"
risk.labs[I_h] <- "High"
# P53, RB, DNA damage, cell cycle
Plts <- list()
l1 <- c("SCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_DN",
"MARKEY_RB1_ACUTE_LOF_UP",
"REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS",
"REACTOME_CELL_CYCLE_MITOTIC")
df <- data.frame(t(metabric.c2[l1,]), risk.labs)
for (i in 1:4) {
Plts[[i]] <- ggviolin(df, x="risk.labs", y=l1[i], color="risk.labs",
add="jitter") +
labs(y="ssGSEA score", title=str_wrap(gsub("_"," ", l1[i]), width=30))
}
pdf(file="./../Figures/MBRisk_P53RBDNACC.pdf", width=12, height=4)
ggpubr::ggarrange(plotlist=ggpar(Plts, xlab = FALSE, font.main=c(9,"bold","black"), legend="none"), nrow=1)
dev.off()
# Metastasis and others
Plts <- list()
l1 <- c("RAMASWAMY_METASTASIS_UP",
"SARRIO_EPITHELIAL_MESENCHYMAL_TRANSITION_UP",
"YU_MYC_TARGETS_UP",
"STEIN_ESRRA_TARGETS_RESPONSIVE_TO_ESTROGEN_DN")
df <- data.frame(t(metabric.c2[l1,]), risk.labs)
for (i in 1:4) {
Plts[[i]] <- ggviolin(df, x="risk.labs", y=l1[i], color="risk.labs",
add="jitter") +
labs(y="ssGSEA score", title=str_wrap(gsub("_"," ", l1[i]), width=30))
}
pdf(file="./../Figures/MBRisk_MetaSign.pdf", width=12, height=4)
ggpubr::ggarrange(plotlist=ggpar(Plts, xlab = FALSE, font.main=c(9,"bold","black"), legend="none"), nrow=1)
dev.off()
# Tumor-ECM interactions
Plts <- list()
l1 <- c("REACTOME_TYPE_I_HEMIDESMOSOME_ASSEMBLY",
"PID_INTEGRIN4_PATHWAY",
"REACTOME_LAMININ_INTERACTIONS",
"NABA_BASEMENT_MEMBRANES")
df <- data.frame(t(metabric.c2[l1,]), risk.labs)
for (i in 1:4) {
Plts[[i]] <- ggviolin(df, x="risk.labs", y=l1[i], color="risk.labs",
add="jitter") +
labs(y="ssGSEA score", title=str_wrap(gsub("_"," ", l1[i]), width=30))
}
pdf(file="./../Figures/MBRisk_ECM.pdf", width=12, height=4)
ggpubr::ggarrange(plotlist=ggpar(Plts, xlab = FALSE, font.main=c(9,"bold","black"), legend="none"), nrow=1)
dev.off()
#### Top 5 pathways by database
# REACTOME
Plts <- list()
df <- data.frame(t(metabric.c2[l1,]), risk.labs)
for (i in 1:5) {
Plts[[i]] <- ggviolin(df, x="risk.labs", y=l1[i], color="risk.labs",
add="jitter") +
labs(y="ssGSEA score", title=str_wrap(gsub("_"," ", l1[i]), width=25))
}
pdf(file="./../Figures/MBRisk_REACTOME.pdf", width=15, height=5)
ggpubr::ggarrange(plotlist=ggpar(Plts, xlab = FALSE, font.main=c(10,"bold","black"), legend="none"), nrow=1)
dev.off()
# BIOCARTA
Plts <- list()
df <- data.frame(t(metabric.c2[l2,]), risk.labs)
for (i in 1:5) {
Plts[[i]] <- ggviolin(df, x="risk.labs", y=l2[i], color="risk.labs",
add="jitter") +
labs(y="ssGSEA score", title=str_wrap(gsub("_"," ", l2[i]), width=40))
}
pdf(file="./../Figures/MBRisk_BIOCARTA.pdf", width=15, height=5)
ggpubr::ggarrange(plotlist=ggpar(Plts, xlab = FALSE, font.main=c(10,"bold","black"), legend="none"), nrow=1)
dev.off()
# KEGG
Plts <- list()
df <- data.frame(t(metabric.c2[l3,]), risk.labs)
for (i in 1:5) {
Plts[[i]] <- ggviolin(df, x="risk.labs", y=l3[i], color="risk.labs",
add="jitter") +
labs(y="ssGSEA score", title=str_wrap(gsub("_"," ", l3[i]), width=25))
}
pdf(file="./../Figures/MBRisk_KEGG.pdf", width=15, height=5)
ggpubr::ggarrange(plotlist=ggpar(Plts, xlab = FALSE, font.main=c(10,"bold","black"), legend="none"), nrow=1)
dev.off()
# PID
Plts <- list()
df <- data.frame(t(metabric.c2[l4,]), risk.labs)
for (i in 1:5) {
Plts[[i]] <- ggviolin(df, x="risk.labs", y=l4[i], color="risk.labs",
add="jitter") +
labs(y="ssGSEA score", title=str_wrap(gsub("_"," ", l4[i]), width=40))
}
pdf(file="./../Figures/MBRisk_PID.pdf", width=15, height=5)
ggpubr::ggarrange(plotlist=ggpar(Plts, xlab = FALSE, legend="none", font.main=c(10,"bold","black")), nrow=1)
dev.off()
######### Compare meta-PCNA proliferation signature -----------
install.packages("ProliferativeIndex")
library(ProliferativeIndex)
temp <- readDataForPI(data.frame(metabric.exp), modelIDs = ENDORSE)
metabric.PI <- calculatePI(temp)
summary(lm(E2.risk ~ metabric.PI))
plot(E2.risk, metabric.PI); abline(lm(metabric.PI ~ E2.risk))
boxplot(metabric.PI ~ E2.risk.grps)
df <- data.frame("Time"=as.numeric(metabric.annotV2$OS_MONTHS),
"Event"=as.numeric(metabric.annotV2$OS_EVENT),
"RiskGroup"=E2.risk.grps,
"Risk"=E2.risk,
"KI67"=metabric.exp["MKI67",],
"PI"=metabric.PI)
F1 <- coxph(Surv(Time, Event) ~ Risk, data=df, x=T)
F2 <- coxph(Surv(Time, Event) ~ PI, data=df, x=T)
plrtest(F1, F2, nested = F)
F3 <- coxph(Surv(Time, Event) ~ KI67, data=df, x=T)
plrtest(F1, F3, nested = F)
CoxExport(F1, fname="./../Output/METABRIC.ProlIndex1.cfit.csv")
CoxExport(F2, fname="./../Output/METABRIC.ProlIndex2.cfit.csv")
CoxExport <- function(cfit, fname) {
temp <- summary(cfit)
df1 <- data.frame(temp$coefficients)
df2 <- data.frame(temp$conf.int)
c.table <- data.frame("Variable"=rownames(df1),
"HR"=signif(df1$exp.coef., 3),
"95%CI"=paste(signif(df2$lower..95, 3), signif(df2$upper..95, 3), sep=" - "),
"P"=signif(df1$Pr...z.., 2))
write.csv(c.table, file=fname)
}
summary(coxph(Surv(Time, Event) ~ PI, data=df))
summary(coxph(Surv(Time, Event) ~ PI + RiskGroup, data=df))