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This code is to look at the EGFR exon imbalance. Therefore, we need the count data, rsem data, and exon data
LUAD Genes RSEM
rsemdatanormalized=read.table("data/tcga_luad_expression/luadrsemdata/gdac.broadinstitute.org_LUAD.Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.Level_3.2016012800.0.0/LUAD.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.data.txt",sep = "\t",header = T,stringsAsFactors = F)
egfr_rsem=data.frame(t(rsemdatanormalized[grepl("^egfr\\|",rsemdatanormalized$Hybridization.REF,ignore.case = T),])[-1,])
#410 of the 577 patients have an RSEM higher than 410
colnames(egfr_rsem)[1]="RSEM_normalized"
egfr_rsem$Patid=rownames(egfr_rsem)
#Standardizing Patid Names
egfr_rsem$Patid=substring(egfr_rsem$Patid,first = 1,last = 12)
egfr_rsem$Patid=gsub("\\.","-",egfr_rsem$Patid)
# # As Character
egfr_rsem[colnames(egfr_rsem)] <- lapply(egfr_rsem[colnames(egfr_rsem)],as.character)
# # As Numeric: Converting from list to numeric
egfr_rsem$RSEM_normalized=unlist(egfr_rsem$RSEM_normalized)
egfr_rsem$RSEM_normalized=as.numeric(egfr_rsem$RSEM_normalized)LUAD Count data:
#Non-normalized:
gene_expression_data=read.table("data/tcga_luad_expression/luadgeneexpression/gdac.broadinstitute.org_LUAD.Merge_rnaseq__illuminahiseq_rnaseq__unc_edu__Level_3__gene_expression__data.Level_3.2016012800.0.0/LUAD.rnaseq__illuminahiseq_rnaseq__unc_edu__Level_3__gene_expression__data.data.txt",sep = "\t",header = T,stringsAsFactors = F)
#Normalized
# gene_expression_data=read.table(,sep = "\t",header = T,stringsAsFactors = F)
#Finding egfr
egfr_gene_exp=rbind(gene_expression_data[1,],gene_expression_data[grepl("^egfr\\|",gene_expression_data$Hybridization.REF,ignore.case = T),])
#Removing Columns for Median_length_normalized and RPKM
t_egfr_gene_exp=data.frame(t(egfr_gene_exp[,grepl("raw_count",egfr_gene_exp[1,])]))
#Counting patients with raw reads >500
# sum(as.numeric(as.numeric(as.character(t_egfr_gene_exp$X580))>=500))
#ONLY 6 PATIENTS HAVE RAW COUNTS OF >500LUAD Exon RPKM This creates a .csv file and only needs to be run once.
# rm(list=ls())#Clears workspace
exondatacomb=read.table("data/tcga_luad_expression/luadexondatacomb/gdac.broadinstitute.org_LUAD.Merge_rnaseq__illuminahiseq_rnaseq__unc_edu__Level_3__exon_expression__data.Level_3.2016012800.0.0/LUAD.rnaseq__illuminahiseq_rnaseq__unc_edu__Level_3__exon_expression__data.data.txt",stringsAsFactors=FALSE,header=TRUE, sep="\t",fill=TRUE)
# head(exondatacomb)
# Obtained EGFR data from here: https://useast.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000146648;r=7:55019021-55211628
#Chromosome 7
exondatachr7=exondatacomb[grep("^chr7:",exondatacomb$Hybridization.REF),] #i.e. it starts with chromosome 7
#EGFR within Chromosome 7
exondatachr7egfr=exondatacomb[c(90511:90542),]
exondatachr7egfr=exondatachr7egfr[grep(":\\+",exondatachr2egfr$Hybridization.REF),] #i.e. it is only on +ive strand
exondatachr7egfr=exondatachr7egfr[c(-4,-16,-17),]
exondatachr7egfr$exon=c(27:1)
alldataegfr=exondatachr7egfr[,c(488,c(2:487))]
#Switching up order
alldataegfr2=alldataegfr[c(27:1),]
#Making the dataframe of a numeric type so that analysis can be carried out on it.
# As Character
alldataegfr[colnames(alldataegfr)] <- lapply(alldataegfr[colnames(alldataegfr)],as.character)
# As Numeric
alldataegfr[colnames(alldataegfr)] <- lapply(alldataegfr[colnames(alldataegfr)],as.numeric)
#Getting the correct column names for alldataegfr
colnames_exondata=exondatacomb[1,]
colnames(colnames_exondata)=colnames(alldataegfr)
alldataegfr=rbind(colnames_exondata,alldataegfr) #Adding first row that contains names of measurements such as RPKM, RSEM, Counts
write.table(alldataegfr,'output/luad_egfr_exon_expression.csv')alldataegfr=read.csv("output/luad_egfr_exon_expression.csv",stringsAsFactors = F,header = T,sep = "",fill = T)
#Getting Count Data
alldataegfr_count=cbind(alldataegfr$exon,alldataegfr[,grepl("raw_counts",alldataegfr[1,])])[-1,]
# As Character
alldataegfr_count[colnames(alldataegfr_count)] <-lapply(alldataegfr_count[colnames(alldataegfr_count)],as.character)
# As Numeric
alldataegfr_count[colnames(alldataegfr_count)] <- lapply(alldataegfr_count[colnames(alldataegfr_count)],as.numeric)
#Sum exons 1:27
egfr_count_data=data.frame(t(data.frame(lapply(alldataegfr_count[c(1:27),],sum))[,-1])) #Not sure if lapply is the right thing to use here. Really messed up way of summing indices in dataframe
colnames(egfr_count_data)="mRNA_count"
alldataegfr_medianlength=cbind(alldataegfr$exon,alldataegfr[,grepl("median_length",alldataegfr[1,])])
# As Character
alldataegfr_medianlength=alldataegfr_medianlength[-1,] #Removing the first row. May be unnecessary in the future
alldataegfr_medianlength[colnames(alldataegfr_medianlength)] <- lapply(alldataegfr_medianlength[colnames(alldataegfr_medianlength)],as.character)
# As Numeric
alldataegfr_medianlength[colnames(alldataegfr_medianlength)] <- lapply(alldataegfr_medianlength[colnames(alldataegfr_medianlength)],as.numeric)
#Sum exons 1:27
egfr_medianlength_data=data.frame(t(data.frame(lapply(alldataegfr_medianlength[c(1:27),],sum))[,-1])) #Removing sum of exons lol
colnames(egfr_medianlength_data)="medianlength"
#Getting RPKM
alldataegfr_RPKM=cbind(alldataegfr$exon,alldataegfr[,grepl("RPKM",alldataegfr[1,])])
# As Character
alldataegfr_RPKM=alldataegfr_RPKM[-1,] #Removing the first row. May be unnecessary in the future
alldataegfr_RPKM[colnames(alldataegfr_RPKM)] <- lapply(alldataegfr_RPKM[colnames(alldataegfr_RPKM)],as.character)
# As Numeric
alldataegfr_RPKM[colnames(alldataegfr_RPKM)] <- lapply(alldataegfr_RPKM[colnames(alldataegfr_RPKM)],as.numeric)
egfr_RPKM_data=data.frame(cbind(lapply(alldataegfr_RPKM[c(1:17),],mean),lapply(alldataegfr_RPKM[c(18:24),],mean))[-1,])
colnames(egfr_RPKM_data)=c("mean_RPKM_1.17","mean_RPKM_18.24")
# As Character
egfr_RPKM_data[colnames(egfr_RPKM_data)] <- lapply(egfr_RPKM_data[colnames(egfr_RPKM_data)],as.character)
# As Numeric
egfr_RPKM_data[colnames(egfr_RPKM_data)] <- lapply(egfr_RPKM_data[colnames(egfr_RPKM_data)],as.numeric)
# Calculating Ratios of exon RPKM means
egfr_RPKM_data$Ratio18.24=egfr_RPKM_data$mean_RPKM_18.24/egfr_RPKM_data$mean_RPKM_1.17
#Changing rownames (patient_ids) to become the same between each other
rownames(egfr_RPKM_data)=substring(rownames(egfr_RPKM_data),first=1,last=28)
rownames(egfr_medianlength_data)=substring(rownames(egfr_medianlength_data),first=1,last=28)
egfr_RPKM_data$Patid=rownames(egfr_RPKM_data)
egfr_medianlength_data$Patid=rownames(egfr_medianlength_data)
egfr_count_data$Patid=rownames(egfr_count_data)
mergetemp=merge(egfr_RPKM_data,egfr_count_data,by="Patid")
egfr_exon_data=merge(mergetemp,egfr_medianlength_data,by="Patid")
#Transforming the Patids so that they're compatible with the Patids in the mutation data
egfr_exon_data$Patid=substring(egfr_exon_data$Patid,first = 1,last = 12)
#Since the names for exon data are not the same format as the mutation data, we're gonna change that here
egfr_exon_data$Patid=gsub("\\.","-",egfr_exon_data$Patid)
egfr_merged_data=merge(egfr_exon_data,egfr_rsem,by="Patid",all=T)
write.csv(egfr_merged_data,"output/all_data_luad_egfr.csv")Making EGFR Expression the plots:
egfr_merged_data=read.csv("output/all_data_luad_egfr.csv")
plotly=ggplot(egfr_merged_data,aes(x=mean_RPKM_1.17, y=mean_RPKM_18.24))+
geom_abline(size=1)+
geom_point(size=1)+
scale_x_continuous(trans = "log10",name="Exon 1:17 RPKM",breaks=c(1e-2,1e0,1e2),labels = parse(text = c("10^-2","10^0","10^2")),limits = c(1e-3,1e3))+
scale_y_continuous(trans = "log10",name="Exon 20:24 RPKM",breaks=c(1e-2,1e0,1e2),labels = parse(text = c("10^-2","10^0","10^2")),limits = c(1e-3,1e3))+
cleanup+
theme(plot.title = element_text(hjust=.5),
text = element_text(size=11,face = "bold"),
axis.title = element_text(face="bold",size="11"),
axis.text=element_text(face="bold",size="11",colour = "black"))+
theme(legend.key.size = unit(30,"pt"))
ggplotly(plotly)ggsave("output/egfr_luad_exonimbalance.pdf",width =3 ,height =2.5 ,units = "in",useDingbats=F)Warning: Removed 389 rows containing missing values (geom_point).###We observed an insignificant difference between the distribution for the 1-17 exons and the 20-24 exons.#Testing if both kinase and ALK expression are different
ks.test(egfr_merged_data$mean_RPKM_1.17,egfr_merged_data$mean_RPKM_18.24)Warning in ks.test(egfr_merged_data$mean_RPKM_1.17,
egfr_merged_data$mean_RPKM_18.24): p-value will be approximate in the
presence of ties
Two-sample Kolmogorov-Smirnov test
data: egfr_merged_data$mean_RPKM_1.17 and egfr_merged_data$mean_RPKM_18.24
D = 0.38397, p-value = 1.332e-15
alternative hypothesis: two-sided###We observed a significant difference between the distribution for the 20-29 exons and the 1-19 exons The reported p-value was 2-16.
###The p-value from a Chi-sq test was 2.2e-16 too
#for all ALK data, not ALKATI
ov_expr_obs=c(sum(as.numeric(egfr_merged_data$Ratio18.24>1),na.rm = T),
dim(egfr_merged_data)[1]-sum(as.numeric(egfr_merged_data$Ratio18.24>1),na.rm = T))
ov_expr_expected=c(round(dim(egfr_merged_data)[1]/2),
round(dim(egfr_merged_data)[1]/2))
overexpression=data.frame(rbind(ov_expr_obs,ov_expr_expected))
# overexpression=data.frame(rbind(c(338,13),c(176,176)))
colnames(overexpression)=c("Yes","No")
chisq.test(overexpression)
Pearson's Chi-squared test with Yates' continuity correction
data: overexpression
X-squared = 18.24, df = 1, p-value = 1.947e-05
sessionInfo()R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggsignif_0.5.0 usethis_1.5.0 devtools_2.0.2
[4] RColorBrewer_1.1-2 reshape2_1.4.3 doParallel_1.0.14
[7] iterators_1.0.10 foreach_1.4.4 dplyr_0.8.1
[10] VennDiagram_1.6.20 futile.logger_1.4.3 workflowr_1.3.0
[13] tictoc_1.0 knitr_1.23 plotly_4.9.0
[16] ggplot2_3.1.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 tidyr_0.8.3 prettyunits_1.0.2
[4] ps_1.3.0 assertthat_0.2.1 rprojroot_1.3-2
[7] digest_0.6.19 mime_0.6 R6_2.4.0
[10] plyr_1.8.4 futile.options_1.0.1 backports_1.1.4
[13] evaluate_0.13 httr_1.4.0 pillar_1.4.0
[16] rlang_0.3.4 lazyeval_0.2.2 data.table_1.12.2
[19] whisker_0.3-2 callr_3.2.0 rmarkdown_1.12
[22] desc_1.2.0 stringr_1.4.0 htmlwidgets_1.3
[25] munsell_0.5.0 shiny_1.3.2 httpuv_1.5.1
[28] compiler_3.5.2 xfun_0.7 pkgconfig_2.0.2
[31] pkgbuild_1.0.3 htmltools_0.3.6 tidyselect_0.2.5
[34] tibble_2.1.1 codetools_0.2-16 viridisLite_0.3.0
[37] later_0.8.0 crayon_1.3.4 withr_2.1.2
[40] xtable_1.8-4 jsonlite_1.6 gtable_0.3.0
[43] git2r_0.25.2 magrittr_1.5 formatR_1.6
[46] scales_1.0.0 cli_1.1.0 stringi_1.4.3
[49] promises_1.0.1 fs_1.3.1 remotes_2.0.4
[52] lambda.r_1.2.3 tools_3.5.2 glue_1.3.1
[55] purrr_0.3.2 crosstalk_1.0.0 processx_3.3.1
[58] pkgload_1.0.2 yaml_2.2.0 colorspace_1.4-1
[61] sessioninfo_1.1.1 memoise_1.1.0