Last updated: 2019-02-18

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Rmd bcb2f86 Briana Mittleman 2019-02-18 add qtl by per and diff iso

I will use this analysis to look at the number of apaQTL genes by the percentile of the counts for the gene. This may help us know if we want to sequence the libraries deeper.

library(workflowr)
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Run ?workflowr for help getting started
library(tidyverse)
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library(cowplot)

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library(reshape2)

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QTL genes

First, upload the QTL gene.

nucQTLs=read.table("../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval", "bh"))

nucQTLsGenes= nucQTLs%>%separate(pid, into=c("chr", "start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_") %>% select(gene) %>% unique()


totQTLs=read.table("../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval", "bh"))

totQTLsGenes= totQTLs%>%separate(pid, into=c("chr", "start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_") %>% select(gene) %>% unique()

How many of the genes overlap:

QTLgene_both=totQTLsGenes %>% inner_join(nucQTLsGenes, by="gene")
nrow(QTLgene_both) 
[1] 141

This means 141 genes have a QTL for both. It does not tell me if the QTL is the same.

Gene Counts

I can get this from the prefiltered peak counts. /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.fc

/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.fc

I can pull these files in, group them by gene and get summaries.

Total

totPeakCounts=read.table("../data/AllPeak_counts/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chr", "start", "end", "strand", "gene"), sep=":") %>% select(-peak, -chr,-start, -end, -strand, -gene)
totPeakCountsGene=read.table("../data/AllPeak_counts/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chr", "start", "end", "strand", "gene"), sep=":") %>% select(gene)
#sum across ind.
totPeakCounts_Sum=rowSums(totPeakCounts)
totPeakCountsGeneSum=as.data.frame(cbind(totPeakCountsGene,totPeakCounts_Sum)) %>% group_by(gene) %>% summarise(TotalCount=sum(totPeakCounts_Sum)) %>% mutate(Percentile = percent_rank(TotalCount))

Subset by percetile:

totalCount_10= totPeakCountsGeneSum %>% filter(Percentile<.1)
totalCount_20= totPeakCountsGeneSum %>% filter(Percentile<.2, Percentile>.1)
totalCount_30= totPeakCountsGeneSum %>% filter(Percentile<.3, Percentile>.2)
totalCount_40= totPeakCountsGeneSum %>% filter(Percentile<.4, Percentile>.3)
totalCount_50= totPeakCountsGeneSum %>% filter(Percentile<.5, Percentile>.4)
totalCount_60= totPeakCountsGeneSum %>% filter(Percentile<.6, Percentile>.5)
totalCount_70= totPeakCountsGeneSum %>% filter(Percentile<.7, Percentile>.6)
totalCount_80= totPeakCountsGeneSum %>% filter(Percentile<.8, Percentile>.7)
totalCount_90= totPeakCountsGeneSum %>% filter(Percentile<.9, Percentile>.8)
totalCount_100= totPeakCountsGeneSum %>% filter(Percentile<1, Percentile>.9)

Nuclear

nucPeakCounts=read.table("../data/AllPeak_counts/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chr", "start", "end", "strand", "gene"), sep=":") %>% select(-peak, -chr,-start, -end, -strand, -gene)
nucPeakCountsGene=read.table("../data/AllPeak_counts/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chr", "start", "end", "strand", "gene"), sep=":") %>% select(gene)
#sum across ind.
nucPeakCounts_Sum=rowSums(nucPeakCounts)
nucPeakCountsGeneSum=as.data.frame(cbind(nucPeakCountsGene,nucPeakCounts_Sum)) %>% group_by(gene) %>% summarise(NuclearCount=sum(nucPeakCounts_Sum)) %>% mutate(Percentile = percent_rank(NuclearCount)) 
nuclearCount_10= nucPeakCountsGeneSum %>% filter(Percentile<.1)
nuclearCount_20= nucPeakCountsGeneSum %>% filter(Percentile<.2, Percentile>.1)
nuclearCount_30= nucPeakCountsGeneSum %>% filter(Percentile<.3, Percentile>.2)
nuclearCount_40= nucPeakCountsGeneSum %>% filter(Percentile<.4, Percentile>.3)
nuclearCount_50= nucPeakCountsGeneSum %>% filter(Percentile<.5, Percentile>.4)
nuclearCount_60= nucPeakCountsGeneSum %>% filter(Percentile<.6, Percentile>.5)
nuclearCount_70= nucPeakCountsGeneSum %>% filter(Percentile<.7, Percentile>.6)
nuclearCount_80= nucPeakCountsGeneSum %>% filter(Percentile<.8, Percentile>.7)
nuclearCount_90= nucPeakCountsGeneSum %>% filter(Percentile<.9, Percentile>.8)
nuclearCount_100= nucPeakCountsGeneSum %>% filter(Percentile<1, Percentile>.9)

QTL genes

I can get the percentile for each QTL gene.

totQTLGene_Perc= totQTLsGenes %>% inner_join(totPeakCountsGeneSum, by="gene") %>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n())
nucQTLGene_Perc= nucQTLsGenes %>% inner_join(nucPeakCountsGeneSum, by="gene")%>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n())

bothPerc=totQTLGene_Perc %>% full_join(nucQTLGene_Perc,  by = c("roundPerc"))
bothPerc$Ngenes.x= bothPerc$Ngenes.x %>% replace_na(0)
colnames(bothPerc)= c("Percentile", "Total", "Nuclear")


bothPerc_melt= melt(bothPerc, id.vars = "Percentile") 
colnames(bothPerc_melt) =c("Percentile", "Fraction", "Genes")

Plot:

ggplot(bothPerc_melt, aes(x=Percentile, y= Genes, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position = "dodge") + theme(axis.text.y = element_text(size=12),axis.title.y=element_text(size=10,face="bold"), axis.title.x=element_text(size=12,face="bold"))+ scale_fill_manual(values=c("darkviolet","deepskyblue3"))

To make is a percente of genes in the category I will divide each number of genes category by the number of total QTL genes. For total this is 291 and for nuclear it is 496

totQTLGene_Perc_prop= totQTLsGenes %>% inner_join(totPeakCountsGeneSum, by="gene") %>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n()) %>% mutate(PercGenes=Ngenes/291)
nucQTLGene_Perc_prop= nucQTLsGenes %>% inner_join(nucPeakCountsGeneSum, by="gene")%>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n()) %>%mutate(PercGenes=Ngenes/496)


bothPercProp=totQTLGene_Perc_prop %>% full_join(nucQTLGene_Perc_prop,  by = c("roundPerc")) %>% select(roundPerc, starts_with("perc"))
bothPercProp$PercGenes.x= bothPercProp$PercGenes.x %>% replace_na(0)
colnames(bothPercProp)= c("Percentile", "Total", "Nuclear")


bothPercPrp_melt= melt(bothPercProp, id.vars = "Percentile") 
colnames(bothPercPrp_melt) =c("Percentile", "Fraction", "GenesProp")
QTLSbyCountPerc=ggplot(bothPercPrp_melt, aes(x=Percentile, y=GenesProp, fill=Fraction)) +geom_bar(stat="identity", position = "dodge")+labs(title="Proportion of QTL genes by Read count percentile",y="Proportion of QTLs", x="Read Cound percentile") +  theme(axis.text.y = element_text(size=12),axis.title.y=element_text(size=10,face="bold"), axis.title.x=element_text(size=12,face="bold"))+ scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + facet_grid(~Fraction)
QTLSbyCountPerc

ggsave(QTLSbyCountPerc, file="../output/plots/QTLSbyCountPerc.png")
Saving 7 x 5 in image

Plot the distribution of the percentile:

totQTLGene_Percentile= totQTLsGenes %>% inner_join(totPeakCountsGeneSum, by="gene") 

ggplot(totQTLGene_Percentile,aes( x=Percentile)) + geom_histogram(bins=50)

nucQTLGene_Percentile= nucQTLsGenes %>% inner_join(nucPeakCountsGeneSum, by="gene")
ggplot(nucQTLGene_Percentile,aes( x=Percentile)) + geom_histogram(bins=50)



sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  reshape2_1.4.3  cowplot_0.9.3   forcats_0.3.0  
 [5] stringr_1.4.0   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1
[13] workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4 haven_1.1.2      lattice_0.20-35  colorspace_1.3-2
 [5] htmltools_0.3.6  yaml_2.2.0       rlang_0.2.2      pillar_1.3.0    
 [9] glue_1.3.0       withr_2.1.2      modelr_0.1.2     readxl_1.1.0    
[13] bindr_0.1.1      plyr_1.8.4       munsell_0.5.0    gtable_0.2.0    
[17] cellranger_1.1.0 rvest_0.3.2      evaluate_0.13    labeling_0.3    
[21] knitr_1.20       broom_0.5.0      Rcpp_0.12.19     scales_1.0.0    
[25] backports_1.1.2  jsonlite_1.6     fs_1.2.6         hms_0.4.2       
[29] digest_0.6.17    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[33] cli_1.0.1        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[37] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[41] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.11   httr_1.3.1      
[45] rstudioapi_0.9.0 R6_2.3.0         nlme_3.1-137     git2r_0.24.0    
[49] compiler_3.5.1