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geneExprCorr.R
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geneExprCorr.R
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# script to correlate MYCN & c-MYC with BPTF across cell lines, patients and PDXs
# also check correlation with CRC (Core Regulatory Circuit) genes
# setwd("~/KP/BPTF")
library(tidyverse)
library(ggpubr)
# READ DATA ---------------
# ...read data: Cell Line data -----------
# ...read data: Patient data -----------
# TARGET
# GMKF
# ...read data: PDX data -----------
# to keep only NBL PDXs
PPTC_FPKM_hg19_Wheeler_subtracted_mData <- PPTC_FPKM_hg19_Wheeler_subtracted_mData[PPTC_FPKM_hg19_Wheeler_subtracted_mData$CANCER_TYPE_DETAILED == "Neuroblastoma",]
# READ Gene list ---------------
CRC = c("MYCN","PHOX2B","TBX2","ASCL1","HAND2","LMO1","GATA3","ISL1", "MYC", "BPTF","GATA2")
# WRANGLE & SUBSET DATA ---------------
# subset metadata
TARGET_NBL_FPKM_PST_mData <- TARGET_NBL_FPKM_PST_mData[,c(2,14)]
names(TARGET_NBL_FPKM_PST_mData)[2] <- 'MYCN_Status'
PPTC_FPKM_hg19_Wheeler_subtracted_mData <- PPTC_FPKM_hg19_Wheeler_subtracted_mData[,c(1,6)]
gm <- gm %>%
column_to_rownames(var = 'gene')
gabby.clinical <- gabby.clinical %>%
rownames_to_column(var = 'Sample') %>%
select(Sample, mycn_status)
# function definition
wrangleData <- function(df, df.meta, keyname){
new <- df %>%
rownames_to_column(var = 'Gene') %>%
filter(Gene %in% CRC) %>%
gather(key = !!keyname, value = 'FPKM', -Gene)
# merge with metadata
new <- merge(new, df.meta, by = eval(keyname))
# reshape data
new <- new %>%
spread(key = 'Gene', value = 'FPKM')
return(new)
}
# function call
cl <- wrangleData(df = STAR_FPKM_40cells_genes,
df.meta = cellline_mData,
keyname = 'CellLine')
target <- wrangleData(df = TARGET_NBL_FPKM_PST_data,
df.meta = TARGET_NBL_FPKM_PST_mData,
keyname = 'TARGET.ID')
gmkf <- wrangleData(df = gm,
df.meta = gabby.clinical,
keyname = 'Sample')
pdx <- wrangleData(df = PPTC_FPKM_hg19_Wheeler_subtracted_data,
df.meta = PPTC_FPKM_hg19_Wheeler_subtracted_mData,
keyname = 'MODEL')
# reshape data frames
cl <- cl %>%
gather(key = 'Genes', value = 'FPKM', -c(1:4,6))
target <- target %>%
gather(key = 'Genes', value = 'FPKM', -c(1,2,4))
gmkf <- gmkf %>%
gather(key = 'Genes', value = 'FPKM', -c(1,2,4))
pdx <- pdx %>%
gather(key = 'Genes', value = 'FPKM', -c(1,2,4))
# Correcting MYCN status column ------------
target$MYCN_Status <- gsub('single_copy', 'Non-amplified', target$MYCN_Status)
target$MYCN_Status <- gsub('amplified', 'Amplified', target$MYCN_Status)
gmkf$mycn_status <- gsub('not amplified', 'Non-amplified', gmkf$mycn_status)
gmkf$mycn_status <- gsub('amplified', 'Amplified', gmkf$mycn_status)
gmkf$mycn_status <- gsub('unknown', 'Unknown', gmkf$mycn_status)
pdx$MYCN_Status <- gsub('amp', 'Amplified', pdx$MYCN_Status)
pdx$MYCN_Status <- gsub('non-amp', 'Non-amplified', pdx$MYCN_Status)
# plot correlations ---------
# ...cell lines ----------
ggplot(cl, aes(reorder(CellLine,BPTF), BPTF, fill = MYCN_Status)) +
geom_bar(stat = 'identity') +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
# 1. MYCN ~ BPTF correlation
# fixing order of facets
cl$Genes = factor(cl$Genes, levels=c("MYCN","MYC","ASCL1","GATA3","GATA2","HAND2","ISL1" ,"LMO1", "PHOX2B", "TBX2"))
p1 <- ggplot(cl, aes(FPKM, BPTF, color = MYCN_Status)) +
geom_point() +
theme_bw() +
geom_smooth(method=lm, se=FALSE) +
stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
labs(x = '', y = 'BPTF expression') +
facet_wrap(~ Genes, ncol = 3, scales = 'free_x') +
theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
ggsave(p1, filename = paste0(Sys.Date(),'_correlation_cl_CRC_bptf_mycnStatus.pdf'), width = 10, height = 10)
#' #' 2. cMYC ~ BPTF correlation
#' p2ggplot(cl, aes(MYC, BPTF, color = MYCN_Status)) +
#' geom_point() +
#' theme_bw() +
#' geom_smooth(method=lm, se=FALSE) +
#' stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
#' labs(x = '', y = 'BPTF expression') +
#' facet_wrap(~ Genes, ncol = 2, scales = 'free_x') +
#' theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
#' ggsave(p2, filename = paste0(Sys.Date(),'_correlation_cl_cmyc_bptf_mycnStatus.pdf'), width = 10, height = 7)
# ...Patients = TARGET --------------
# 1. MYCN ~ BPTF correlation
target$Genes = factor(target$Genes, levels=c("MYCN","MYC","ASCL1","GATA3","GATA2","HAND2","ISL1" ,"LMO1", "PHOX2B", "TBX2"))
p3 <- ggplot(target, aes(FPKM, BPTF, color = MYCN_Status)) +
geom_point() +
theme_bw() +
geom_smooth(method=lm, se=FALSE) +
stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
labs(x = '', y = 'BPTF expression') +
facet_wrap(~ Genes, ncol = 3, scales = 'free_x') +
theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
ggsave(p3, filename = paste0(Sys.Date(),'_correlation_target_mycn_bptf_mycnStatus.pdf'), width = 10, height = 10)
#' 2. cMYC ~ BPTF correlation
# p4 <- ggplot(target, aes(MYC, BPTF, color = MYCN_Status)) +
# geom_point() +
# theme_bw() +
# xlim(0,300) +
# geom_smooth(method=lm, se=FALSE) +
# stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
# labs(x = 'MYC expression', y = 'BPTF expression') +
# theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
# ggsave(p4, filename = paste0(Sys.Date(),'_correlation_target_cmyc_bptf_mycnStatus.pdf'), width = 10, height = 7)
# ...Patients = GMKF ----------------
# 1. MYCN ~ BPTF correlation
gmkf$Genes = factor(gmkf$Genes, levels=c("MYCN","MYC","ASCL1","GATA3","GATA2","HAND2","ISL1" ,"LMO1", "PHOX2B", "TBX2"))
p5 <- ggplot(gmkf, aes(FPKM, BPTF, color = mycn_status)) +
geom_point() +
theme_bw() +
geom_smooth(method=lm, se=FALSE) +
stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
labs(x = '', y = 'BPTF expression') +
facet_wrap(~ Genes, ncol = 3, scales = 'free_x') +
theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
ggsave(p5, filename = paste0(Sys.Date(),'_correlation_GMKF_mycn_bptf_mycnStatus.pdf'), width = 10, height = 10)
#' 2. cMYC ~ BPTF correlation
# p6 <- ggplot(gmkf, aes(MYC, BPTF, color = mycn_status)) +
# geom_point() +
# theme_bw() +
# xlim(0,150) +
# geom_smooth(method=lm, se=FALSE) +
# stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
# labs(x = 'MYC expression', y = 'BPTF expression') +
# theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
# ggsave(p6, filename = paste0(Sys.Date(),'_correlation_GMKF_cmyc_bptf_mycnStatus.pdf'), width = 10, height = 7)
# ...PDX ----------------
# COG-N-589x is non-amp
# 1. MYCN ~ BPTF correlation
pdx$Genes = factor(pdx$Genes, levels=c("MYCN","MYC","ASCL1","GATA3","GATA2","HAND2","ISL1" ,"LMO1", "PHOX2B", "TBX2"))
p7 <- ggplot(pdx, aes(FPKM, BPTF, color = MYCN_Status)) +
geom_point() +
theme_bw() +
geom_smooth(method=lm, se=FALSE) +
stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
labs(x = '', y = 'BPTF expression') +
facet_wrap(~ Genes, ncol = 3, scales = 'free_x') +
theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
ggsave(p7, filename = paste0(Sys.Date(),'_correlation_PDX_mycn_bptf_mycnStatus.pdf'), width = 10, height = 7)
#' 2. cMYC ~ BPTF correlation
# p8 <- ggplot(pdx, aes(MYC, BPTF, color = MYCN_Status)) +
# geom_point() +
# theme_bw() +
# geom_smooth(method=lm, se=FALSE) +
# stat_cor(method = "pearson", aes(label = paste(..r.label..,..rr.label..,..p.label.., sep = "~` `~` `~"))) +
# labs(x = 'MYC expression', y = 'BPTF expression') +
# theme(strip.text = element_text(size=8), legend.position = "bottom", legend.title = element_text(size=5))
# ggsave(p8, filename = paste0(Sys.Date(),'_correlation_PDX_cmyc_bptf_mycnStatus.pdf'), width = 10, height = 7)
#