GDCTranscriptionFactors

library(tidyverse)
library(dplyr)
library(ggplot2)
library(gsubfn)
library("yarrr")
library("knitr")

GDCTF <- read.csv("~/Desktop/GDCAnalysis/GDCTF.csv", stringsAsFactors = FALSE)
newGDCTF <- GDCTF %>% 
  ## rename the fields and convert strings to numbers
  rename(SSMAffectedCasesInCohort = X..SSM.Affected.Cases.in.Cohort) %>%
  rename(SSMAffectedCasesAcrossGDC = X..SSM.Affected.Cases.Across.the.GDC) %>%
  rename(CNVGain = X..CNV.Gain) %>%
  rename(CNVLoss = X..CNV.Loss) %>%
  rename(Mutations = X..Mutations) %>%
  mutate(Mutations = gsub(",", "", Mutations)) %>%
  mutate(Mutations= as.numeric(Mutations)) %>%
  ## sub the percentage only
  mutate(SSMAffectedCasesInCohort = gsub(".+\\(", "", SSMAffectedCasesInCohort)) %>%
  mutate(SSMAffectedCasesInCohort = as.numeric(gsub("%\\)", "", SSMAffectedCasesInCohort))) %>%
  
  mutate(CNVLoss = gsub(".+\\(", "", CNVLoss)) %>%
  mutate(CNVLoss = as.numeric(gsub("%\\)", "", CNVLoss))) %>%
  
  mutate(CNVGain = gsub(".+\\(", "", CNVGain)) %>%
  mutate(CNVGain = as.numeric(gsub("%\\)", "", CNVGain)))

## rename empty cell no
loop <- 1
while (loop <= nrow(newGDCTF)) {
  if (newGDCTF$Annotations[loop] == "") {
    newGDCTF$Annotations[loop] <- "no"
  } else {
    newGDCTF$Annotations[loop] <- "yes"
  }
  loop <- loop + 1
}

## calculate the percentage
loop <- 1
while (loop <= nrow(newGDCTF)) {
  s <- unlist(strsplit(toString(newGDCTF$SSMAffectedCasesAcrossGDC[loop]), " / "))
  newGDCTF$SSMAffectedCasesAcrossGDC[loop] <- as.numeric(gsub(",", "", s[1])) / as.numeric(gsub(",", "", s[2])) * 100
  loop <- loop + 1
}
## convert strings to numbers
newGDCTF <- newGDCTF %>%
  mutate(SSMAffectedCasesAcrossGDC = as.numeric(SSMAffectedCasesAcrossGDC))

newGDCTF$Annotations <- factor(newGDCTF$Annotations, levels = c('yes', 'no'), ordered = TRUE)

Gene.ID	Symbol	Name	SSMAffectedCasesInCohort	Cytoband	SSMAffectedCasesAcrossGDC	Type	CNVGain	CNVLoss	Mutations	Annotations	Survival
ENSG00000198300	PEG3	paternally expressed 3	7.53	19q13.43	6.910410	protein_coding	4.73	5.36	907	no	NA
ENSG00000164256	PRDM9	PR domain containing 9	6.57	5p14.2	6.028230	protein_coding	4.86	1.52	704	no	NA
ENSG00000116539	ASH1L	ash1 (absent, small, or homeotic)-like (Drosophila)	6.34	1q22	5.822388	protein_coding	10.53	0.91	752	no	NA
ENSG00000121297	TSHZ3	teashirt zinc finger homeobox 3	6.24	19q12	5.724368	protein_coding	6.49	1.40	672	no	NA
ENSG00000080603	SRCAP	Snf2-related CREBBP activator protein	6.17	16p11.2	5.665556	protein_coding	4.45	0.85	743	no	NA
ENSG00000103449	SALL1	spalt-like transcription factor 1	5.86	16q12.1	5.381298	protein_coding	2.48	2.74	647	no	NA
ENSG00000106571	GLI3	GLI family zinc finger 3	5.69	7p14.1	5.224466	protein_coding	2.41	1.86	635	no	NA
ENSG00000186487	MYT1L	myelin transcription factor 1-like	5.56	2p25.3	5.106842	protein_coding	4.79	3.72	609	no	NA
ENSG00000189079	ARID2	AT rich interactive domain 2 (ARID, RFX-like)	5.45	12q12	4.999020	protein_coding	3.28	3.44	617	yes	NA
ENSG00000169946	ZFPM2	zinc finger protein, FOG family member 2	5.20	8q22.3, 8q23.1	4.773574	protein_coding	10.63	2.00	552	no	NA

Plots

## the numbers of SSM detected in that gene
ggplot(newGDCTF, aes(x=Annotations, y=Mutations)) + geom_boxplot() + geom_point() +
  labs(x = "Cancer Gene Census?")

## The number of cases affected by SSMs (simple somatic mutations) in the Cohort.
ggplot(newGDCTF, aes(x=Annotations, y=SSMAffectedCasesInCohort)) + geom_boxplot() + geom_point() +
  labs(x = "Cancer Gene Census?")

## The number of cases within all the projects in the GDC that contain a mutation on this gene.
ggplot(newGDCTF, aes(x=Annotations, y=SSMAffectedCasesAcrossGDC)) + geom_boxplot() + geom_point() +
  labs(x = "Cancer Gene Census?")

## The number of CNV (copy number variation) events detected in that gene which resulted in an increase (gain) in the gene's copy number.
ggplot(newGDCTF, aes(x=Annotations, y=CNVGain)) + geom_boxplot() + geom_point() +
  labs(x = "Cancer Gene Census?")

## The number of CNV events detected in that gene which resulted in a decrease (loss) in the gene's copy number.
ggplot(newGDCTF, aes(x=Annotations, y=CNVLoss)) + geom_boxplot() + geom_point() +
  labs(x = "Cancer Gene Census?")