PAS descriptive plots
Briana Mittleman
4/23/2019
Last updated: 2019-04-24
Checks: 6 0
Knit directory: apaQTL/analysis/
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In this analysis I will create discriptive plots for the PAS identified in the 54 LCLs.
library(workflowr)This is workflowr version 1.3.0
Run ?workflowr for help getting startedlibrary(tidyverse)── Attaching packages ───────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.1.0 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0 ── Conflicts ──────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithslibrary(cowplot)
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsavePeaks per gene:
I want to plot how many genes have 0, 1, 2 and more than 2 PAS in the set. I need to join my PAS with the annotation to find out how many genes have 0 PAS.
pas=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", header = F, stringsAsFactors = F, col.names = c("Chr", "start", "end", "PeakID", "score", "strand")) %>% separate(PeakID, into=c("peaknum", "geneAnno"), sep=":") %>% separate(geneAnno, into=c("Gene", "Loc"),sep="_")
pasbygene= pas %>% group_by(Gene) %>% summarise(PAS=n())
annotation=read.table("../../genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed", col.names = c("chr", "start", "end", "anno", "score", "strand")) %>% separate(anno, into=c("Loc", "Gene"),sep=":") %>% group_by(Gene) %>% summarise(annos=n()) %>% select(Gene)
PASallgene=annotation %>% full_join(pasbygene, by="Gene") %>% replace_na(list(PAS=0))
#group with 0,1,2,more than 2
PASallgene_grouped=PASallgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))Plot this:
Genes=c(sum(PASallgene_grouped$Zero),sum(PASallgene_grouped$One),sum(PASallgene_grouped$Multiple))
PAS=c("Zero", "One", "Multiple")
AllPAS=c(sum(PASallgene_grouped$Zero),sum(PASallgene_grouped$One),sum(PASallgene_grouped$Multiple))
GenebyPAS=as.data.frame(cbind(PAS,AllPAS))
GenebyPAS$AllPAS=as.numeric(as.character(GenebyPAS$AllPAS))
allPASplot=ggplot(GenebyPAS, aes(x="",y=AllPAS, fill=PAS)) + geom_bar(stat="identity", width=.5) + scale_fill_brewer(palette="GnBu") + labs(title="Identified PAS per Gene", y="Genes",x="All Indentified PAS")
allPASplot
ggsave(allPASplot, file="../output/GeneswithAPApotentialAllPAS.png", width=3, height=5)Subset and get stats for UTR
pasUTR=pas %>% filter(Loc=="utr3") %>% group_by(Gene) %>% summarise(PAS=n())
pasUTR_allgene=annotation %>% full_join(pasUTR, by="Gene") %>% replace_na(list(PAS=0))
PASUTRallgene_grouped=pasUTR_allgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))
GenesUTR=c(sum(PASUTRallgene_grouped$Zero),sum(PASUTRallgene_grouped$One),sum(PASUTRallgene_grouped$Multiple))
UTR=c(sum(PASUTRallgene_grouped$Zero),sum(PASUTRallgene_grouped$One),sum(PASUTRallgene_grouped$Multiple))
GenebyPASUTR=as.data.frame(cbind(PAS,UTR))
GenebyPASUTR$UTR=as.numeric(as.character(GenebyPASUTR$UTR))
ggplot(GenebyPASUTR, aes(x="",y=UTR, fill=PAS)) + geom_bar(stat="identity")
Subset and get stats for Intron
pasIntron=pas %>% filter(Loc=="intron" | Loc=='utr3') %>% group_by(Gene) %>% summarise(PAS=n())
pasIntron_allgene=annotation %>% full_join(pasIntron, by="Gene") %>% replace_na(list(PAS=0))
pasIntronallgene_grouped=pasIntron_allgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))
UTRandIntron=c(sum(pasIntronallgene_grouped$Zero),sum(pasIntronallgene_grouped$One),sum(pasIntronallgene_grouped$Multiple))
GenebyPASIntron=as.data.frame(cbind(PAS,UTRandIntron))
GenebyPASIntron$UTRandIntron=as.numeric(as.character(GenebyPASIntron$UTRandIntron))
ggplot(GenebyPASIntron, aes(x="",y=UTRandIntron, fill=PAS)) + geom_bar(stat="identity")
Make these side by side:
GenebyPASUTR_melt=melt(GenebyPASUTR, id.vars = "PAS", value.name = "Genes", variable.name = "Set")
GenebyPAS_melt=melt(GenebyPAS, id.vars = "PAS", value.name = "Genes", variable.name = "Set")
GenebyPASIntron_melt=melt(GenebyPASIntron, id.vars = "PAS", value.name = "Genes", variable.name = "Set")
GenebyPAStoplot=rbind(GenebyPAS_melt,GenebyPASUTR_melt,GenebyPASIntron_melt)
geneswithAPA=ggplot(GenebyPAStoplot, aes(x=Set,y=Genes, fill=PAS, by=Set)) + geom_bar(stat="identity")+ scale_fill_brewer(palette="YlGnBu") + labs(title="Genes with APA poential")
geneswithAPA
ggsave(geneswithAPA, file="../output/GeneswithAPApotential.png")Saving 7 x 5 in imageGenebyPAStoplot PAS Set Genes
1 Zero AllPAS 11659
2 One AllPAS 4111
3 Multiple AllPAS 11345
4 Zero UTR 14503
5 One UTR 6996
6 Multiple UTR 5616
7 Zero UTRandIntron 13025
8 One UTRandIntron 4328
9 Multiple UTRandIntron 9762Location of PAS
PAS_loc=pas %>% group_by(Loc) %>% summarise(nPAS=n())
loclabel=c("Coding", "Downstream", "Intronic", "3' UTR", "5' UTR")
PASLocPlot=ggplot(PAS_loc, aes(x=Loc, y=nPAS, fill=Loc)) + geom_bar(stat="identity",width=.5)+ scale_fill_brewer(palette = "YlGnBu") + labs(x="Gene location", y="Number of identified PAS", title="Location distribution for identified PAS") + theme(legend.position = "none")+ scale_x_discrete(labels= loclabel)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
PASLocPlot
ggsave(PASLocPlot, file="../output/PASlocation.png")Saving 7 x 5 in image
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_0.9.4 reshape2_1.4.3 forcats_0.3.0 stringr_1.3.1
[5] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3
[9] tibble_2.1.1 ggplot2_3.1.0 tidyverse_1.2.1 workflowr_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 RColorBrewer_1.1-2 cellranger_1.1.0
[4] pillar_1.3.1 compiler_3.5.1 git2r_0.23.0
[7] plyr_1.8.4 tools_3.5.1 digest_0.6.18
[10] lubridate_1.7.4 jsonlite_1.6 evaluate_0.12
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-38
[16] pkgconfig_2.0.2 rlang_0.3.1 cli_1.0.1
[19] rstudioapi_0.10 yaml_2.2.0 haven_1.1.2
[22] withr_2.1.2 xml2_1.2.0 httr_1.3.1
[25] knitr_1.20 hms_0.4.2 generics_0.0.2
[28] fs_1.2.6 rprojroot_1.3-2 grid_3.5.1
[31] tidyselect_0.2.5 glue_1.3.0 R6_2.3.0
[34] readxl_1.1.0 rmarkdown_1.10 modelr_0.1.2
[37] magrittr_1.5 whisker_0.3-2 backports_1.1.2
[40] scales_1.0.0 htmltools_0.3.6 rvest_0.3.2
[43] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3
[46] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0
[49] broom_0.5.1 crayon_1.3.4