Last updated: 2019-02-19

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File Version Author Date Message
Rmd e0461e4 Briana Mittleman 2019-02-19 add plots filtered by gene with 2 peaks
html 4ea438e Briana Mittleman 2019-02-18 Build site.
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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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"))  

Version Author Date
4ea438e Briana Mittleman 2019-02-18

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

Version Author Date
4ea438e Briana Mittleman 2019-02-18
ggsave(QTLSbyCountPerc, file="../output/plots/QTLSbyCountPerc.png")
Saving 7 x 5 in image

Filter to only look at genes with at least 2 peaks and build the percentiles off these. To do this I will filter the unfiltered peak counts by genes with two peaks in the 5% peaks used in the QTL analysis.

Pull in filtered peaks:

Peaks=read.table("../data/PeakUsage_noMP_GeneLocAnno/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed", stringsAsFactors = F, col.names = c("chr", 'start', 'end', 'id', 'score', 'strand')) 
Genes2Peaks= Peaks %>% separate(id, into=c("gene", "peak"), sep=":") %>% group_by(gene) %>% summarise(nPeak=n()) %>% filter(nPeak>=2) %>% select(gene)

Now filter the total and nuclear peaks that are in these genes before making the percentile plots.

Total

totPeakCounts_FiltGene=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=":") %>% semi_join(Genes2Peaks, by="gene")%>% select(-peak, -chr,-start, -end, -strand, -gene)

totPeakCountsFiltGene=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=":") %>% semi_join(Genes2Peaks, by="gene") %>% select(gene)
#sum across ind.
totPeakCounts_FiltSum=rowSums(totPeakCounts_FiltGene)
totPeakCountsFiltGeneSum=as.data.frame(cbind(totPeakCountsFiltGene,totPeakCounts_FiltSum)) %>% group_by(gene) %>% summarise(TotalCount=sum(totPeakCounts_FiltSum)) %>% mutate(Percentile = percent_rank(TotalCount)) 

Nuclear

nucPeakCounts_FiltGene=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=":") %>% semi_join(Genes2Peaks, by="gene")%>% select(-peak, -chr,-start, -end, -strand, -gene)

nucPeakCountsFiltGene=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=":") %>% semi_join(Genes2Peaks, by="gene") %>% select(gene)
#sum across ind.
nucPeakCounts_FiltSum=rowSums(nucPeakCounts_FiltGene)
nucPeakCountsFiltGeneSum=as.data.frame(cbind(nucPeakCountsFiltGene,nucPeakCounts_FiltSum)) %>% group_by(gene) %>% summarise(NuclearCount=sum(nucPeakCounts_FiltSum)) %>% mutate(Percentile = percent_rank(NuclearCount)) 

I can get the percentile for each QTL gene.

totQTLFiltGene_Perc= totQTLsGenes %>% inner_join(totPeakCountsFiltGeneSum, by="gene") %>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n())
nucQTLFiltGene_Perc= nucQTLsGenes %>% inner_join(nucPeakCountsFiltGeneSum, by="gene")%>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n())

bothPerc_filt=totQTLFiltGene_Perc %>% full_join(nucQTLFiltGene_Perc,  by = c("roundPerc"))
bothPerc_filt$Ngenes.x= bothPerc_filt$Ngenes.x %>% replace_na(0)
colnames(bothPerc_filt)= c("Percentile", "Total", "Nuclear")


bothPercfilt_melt= melt(bothPerc_filt, id.vars = "Percentile") 
colnames(bothPercfilt_melt) =c("Percentile", "Fraction", "Genes")

Plot:

ggplot(bothPercfilt_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"))  

Do it by percent

totQTLFiltGene_Perc_prop= totQTLsGenes %>% inner_join(totPeakCountsFiltGeneSum, by="gene") %>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n()) %>% mutate(PercGenes=Ngenes/291)
nucQTLFiltGene_Perc_prop= nucQTLsGenes %>% inner_join(nucPeakCountsFiltGeneSum, by="gene")%>% mutate(roundPerc=round(Percentile, digits=1)) %>% group_by(roundPerc) %>% summarise(Ngenes=n()) %>%mutate(PercGenes=Ngenes/496)


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


bothPercPrpFilt_melt= melt(bothPercFiltProp, id.vars = "Percentile") 
colnames(bothPercPrpFilt_melt) =c("Percentile", "Fraction", "GenesProp")
QTLSbyCountPerc_filt=ggplot(bothPercPrpFilt_melt, aes(x=Percentile, y=GenesProp, fill=Fraction)) +geom_bar(stat="identity", position = "dodge")+labs(title="Proportion of QTL genes by Read count percentile\n Genes with 2 peaks in QTL analysis",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_filt

ggsave(QTLSbyCountPerc_filt, file="../output/plots/QTLSbyCountPerc_Genes2Peaks.png")
Saving 7 x 5 in image


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