Commit 8f9e2532 authored by Venkat Malladi's avatar Venkat Malladi

Update with figures for PARP-1 and DDX21.

parent b97cf3bb
......@@ -96,3 +96,76 @@ p <- ggplot(aes(y = log2(SNGH4+1), x = PAM, fill= PAM ), data = df_SNGH4_pam,) +
jpeg('figures/SNGH4_breast_correlation_pam.jpg')
p
dev.off()
# Look at PARP1 and DDX21
#PARP1 ENSG00000143799.12
#DDX21 ENSG00000165732.12
pd_gtex <- data.frame(t(rpkm_breast_gtex[which(rownames(rpkm_breast_gtex) %in% c("ENSG00000143799.12","ENSG00000165732.12")),]))
colnames(pd_gtex) <- c('PARP1','DDX21')
phen <- data.frame(rse_tcga$bigwig_file,rse_tcga$gdc_cases.project.primary_site, rse_tcga$cgc_sample_sample_type,rse_tcga$cgc_case_pathologic_stage, rse_tcga$xml_breast_carcinoma_estrogen_receptor_status, rse_tcga$xml_breast_carcinoma_progesterone_receptor_status,rse_tcga$gdc_cases.samples.submitter_id)
colnames(phen) <- c('Experiment', 'Site', 'Type', 'Stage', 'ER', 'PR','sample')
phen$Experiment <- gsub(".bw", "", phen$Experiment)
rownames(phen) <- phen$Experiment
pd_tcga <- data.frame(t(rpkm_tcga[which(rownames(rpkm_tcga) %in% c("ENSG00000143799.12","ENSG00000165732.12")),]))
colnames(pd_tcga) <- c('PARP1','DDX21')
pd_tcga['Experiment'] <- rownames(pd_tcga)
tt <- merge(phen,pd_tcga)
pd_tcga_breast <- tt[which(tt$Site %in% c('Breast')),]
tcga_meta_data <- colData(data)[c('sample','subtype_PAM50.mRNA')]
colnames(tcga_meta_data) <- c('sample', 'PAM')
pd_tcga_breast_pam <- merge(pd_tcga_breast, tcga_meta_data, by.x=c("sample"), by.y=c("sample"))
# Merge TCGA and GTeX
df_pd_pam <- data.frame(pd_tcga_breast_pam[,c('PARP1','DDX21','PAM','Type')])
pd_gtex$PAM <- "GTEX"
pd_gtex$Type <- "GTEX"
df_pd_tcag_gtex <- rbind(df_pd_pam,pd_gtex)
# Update Metastatic to Primary Tumor Type
df_pd_tcag_gtex$Type[df_pd_tcag_gtex$Type == 'Metastatic'] <- 'Primary Tumor'
df_pd_tcag_gtex_pam <- df_pd_tcag_gtex[complete.cases(df_pd_tcag_gtex[ , 3]),]
df_pd_tcag_gtex_type <- df_pd_tcag_gtex[complete.cases(df_pd_tcag_gtex[ , 4]),]
# Seperate into PAM and type for different genes
df_pd_tcag_gtex_pam$PARP1 <- log2(df_pd_tcag_gtex_pam$PARP1)
df_pd_tcag_gtex_pam$DDX21 <- log2(df_pd_tcag_gtex_pam$DDX21)
## PARP1 and PAM
df_pd_PAM_tcag_gtex_summary <- summarySE(df_pd_tcag_gtex_pam, measurevar="PARP1", groupvars=c("PAM"))
p <- ggplot(df_pd_PAM_tcag_gtex_summary, aes(x = as.factor(PAM), fill= PAM)) +
geom_boxplot(aes(
lower = PARP1 - se,
upper = PARP1 + se,
middle = PARP1,
ymin = PARP1 - 2*se,
ymax = PARP1 + 2*se),
stat = "identity",lwd=1.5) + geom_hline(yintercept = mean(df_pd_tcag_gtex_pam$PARP1), linetype = 2) + labs(y="RPKM",x="PAM") + theme_bw() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),axis.ticks=element_line(size=1)) + scale_y_continuous(limits=c(1.5,2.5))
jpeg('figures/PARP1_breast_correlation_PAM_boxplot.jpg')
p
dev.off()
parp1_pam.aov = aov(PARP1 ~ PAM,data=df_pd_tcag_gtex_pam)
summary(parp1_pam.aov)
## PHF8 and PAM
df_ddx21_PAM_tcag_gtex_summary <- summarySE(df_pd_tcag_gtex_pam, measurevar="DDX21", groupvars=c("PAM"))
p <- ggplot(df_ddx21_PAM_tcag_gtex_summary, aes(x = as.factor(PAM), fill= PAM)) +
geom_boxplot(aes(
lower = DDX21 - se,
upper = DDX21 + se,
middle = DDX21,
ymin = DDX21 - 2*se,
ymax = DDX21 + 2*se),
stat = "identity",lwd=1.5) + geom_hline(yintercept = mean(df_pd_tcag_gtex_pam$DDX21), linetype = 2) + labs(y="RPKM",x="PAM") + theme_bw() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),axis.ticks=element_line(size=1)) + scale_y_continuous(limits=c(3,5))
jpeg('figures/DDX21_breast_correlation_PAM_boxplot.jpg')
p
dev.off()
ddx21_pam.aov = aov(DDX21 ~ PAM,data=df_pd_tcag_gtex_pam)
summary(ddx21_pam.aov)
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