apaQTLs by gene count percentile
Briana Mittleman
2/18/2019
Last updated: 2019-02-19
Checks: 6 0
Knit directory: threeprimeseq/analysis/
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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)This is workflowr version 1.2.0
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smithsQTL 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] 141This 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 imageFilter 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