Last updated: 2019-06-13
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/DiffIsoAnalysis.Rmd
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/choosePCs.Rmd
Modified: analysis/corrbetweenind.Rmd
Modified: analysis/index.Rmd
Modified: analysis/mapapaQTL.Rmd
Modified: analysis/motifDisruption.Rmd
Modified: analysis/nascenttranscription.Rmd
Modified: analysis/nucintronicanalysis.Rmd
Modified: analysis/overlapapaqtlsandeqtls.Rmd
Modified: analysis/rna_netseq_h3k12ac.Rmd
Modified: code/BothFracDTPlotGeneRegions.sh
Modified: code/Snakefile
Deleted: code/Upstream10Bases_general.py
Modified: code/apaQTLCorrectPvalMakeQQ.R
Modified: code/apaQTL_permuted.sh
Modified: code/apaQTLsnake.err
Modified: code/bam2bw.sh
Modified: code/bed2saf.py
Modified: code/cluster.json
Modified: code/clusterfiltPAS.json
Modified: code/config.yaml
Modified: code/environment.yaml
Modified: code/makePheno.py
Deleted: code/test.txt
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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 started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1 ✔ 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':
smiths
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
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()) %>% dplyr::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)
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")
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 image
GenebyPAStoplot
PAS Set Genes
1 Zero AllPAS 11659
2 One AllPAS 5361
3 Multiple AllPAS 10095
4 Zero UTR 14594
5 One UTR 7479
6 Multiple UTR 5042
7 Zero UTRandIntron 13089
8 One UTRandIntron 5677
9 Multiple UTRandIntron 8349
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
I want to make a script that takes a cuttoff and tells me how many gens have 0,1, >1 PAS. This way I can put these together to make stacked barplots:
I can make the plot for total then again for nuclear.
The annotaiton is annotation:
totapaanno=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc",header = T,stringsAsFactors = F)
indiv=colnames(totapaanno)[2:55]
totapanum=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.CountsOnlyNumeric",header = F, col.names = indiv)
totapa_mean=rowMeans(totapanum)
totapaMeananno=as.data.frame(cbind(ID=totapaanno$chrom, meanUsage=totapa_mean))
totapaMeananno$meanUsage=as.numeric(as.character(totapaMeananno$meanUsage))
totapaMeananno$ID=as.character(totapaMeananno$ID)
genesbycuttoff_tot=function(fraction){
totapaMeananno_filt=totapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(Gene) %>% summarise(PAS=n())
PASallgene=annotation %>% full_join(totapaMeananno_filt, by="Gene") %>% replace_na(list(PAS=0))
PASallgene_cat=PASallgene %>% mutate(Category=ifelse(PAS==0,"Zero", ifelse(PAS==1, "One", "Multiple"))) %>% group_by(Category) %>% summarise(NPer=n())
return(PASallgene_cat$NPer)
}
#multiple, one, zero
categories=c("Multiple_PAS", "One_PAS", "Zero_PAS")
FullDF=as.data.frame(cbind(categories))
cutoffs=seq(from=0, to=.5, by=.05)
for (val in cutoffs[1:10])
{
FullDF=cbind(FullDF,val=genesbycuttoff_tot(val))
}
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [4801,
4802, 4803].
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3549,
3550, 3551].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2951,
2952].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2573,
2574].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2269].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2024].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1827].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1651].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1521].
colnames(FullDF)=c("Category",cutoffs[1:10])
Melt:
fullDF_melt=melt(FullDF,id.vars = "Category",variable.name = "Cutoff", value.name = "NGenes") %>% mutate(propGene=NGenes/nrow(annotation))
ggplot(fullDF_melt,aes(x=Cutoff, y=propGene, by=Category, fill=Category)) + geom_bar( stat="identity",width = .5) + scale_fill_brewer(palette="GnBu") + labs(title="Proportion of genes with Multiple PAS by Total Usage filter",y="Proportion of 27115 genes", x="Usage Filter cutoff") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_x_discrete(name="Usage Filter cutoff", breaks=c("0","0.1","0.2", "0.3", "0.4","0.5"))
Nuclear:
nucapaanno=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.fc",header = T,stringsAsFactors = F)
nucapanum=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.CountsOnlyNumeric",header = F, col.names = indiv)
nucapa_mean=rowMeans(nucapanum)
nucapaMeananno=as.data.frame(cbind(ID=nucapaanno$chrom, meanUsage=nucapa_mean))
nucapaMeananno$meanUsage=as.numeric(as.character(nucapaMeananno$meanUsage))
nucapaMeananno$ID=as.character(nucapaMeananno$ID)
genesbycuttoff_nuc=function(fraction){
nucapaMeananno_filt=nucapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(Gene) %>% summarise(PAS=n())
PASallgene=annotation %>% full_join(nucapaMeananno_filt, by="Gene") %>% replace_na(list(PAS=0))
PASallgene_cat=PASallgene %>% mutate(Category=ifelse(PAS==0,"Zero", ifelse(PAS==1, "One", "Multiple"))) %>% group_by(Category) %>% summarise(NPer=n())
return(PASallgene_cat$NPer)
}
#multiple, one, zero
FullDFNuc=as.data.frame(cbind(categories))
for (val in cutoffs[1:10])
{
FullDFNuc=cbind(FullDFNuc,val=genesbycuttoff_nuc(val))
}
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [5399,
5400, 5401, 5402].
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3814,
3815, 3816].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [3076,
3077].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2630,
2631].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2310].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2053].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1821].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1645].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1507].
colnames(FullDFNuc)=c("Category",cutoffs[1:10])
FullDFNuc_melt=melt(FullDFNuc,id.vars = "Category",variable.name = "Cutoff", value.name = "NGenes") %>% mutate(propGene=NGenes/nrow(annotation))
ggplot(FullDFNuc_melt,aes(x=Cutoff, y=propGene, by=Category, fill=Category)) + geom_bar( stat="identity") + scale_fill_brewer(palette="GnBu") + labs(title="Proportion of genes with Multiple PAS by Nuclear Usage filter",y="Proportion of 27115 genes", x="Usage Filter cutoff")
Version | Author | Date |
---|---|---|
a88eedf | brimittleman | 2019-05-20 |
I will make these plots but the categories will be location of the PAS.
locbycutoff_tot=function(fraction){
totapaMeananno_filt=totapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(loc) %>% summarise(PerLoc=n()) %>%filter(loc!= "008559")
return(totapaMeananno_filt$PerLoc)
}
locations=c("cds", "end", "intron", "utr3", "utr5")
FullDF_loc=as.data.frame(cbind(locations))
cutoffs=seq(from=0, to=.5, by=.05)
for (val in cutoffs[1:10])
{
FullDF_loc=cbind(FullDF_loc,val=locbycutoff_tot(val))
}
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [4801,
4802, 4803].
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3549,
3550, 3551].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2951,
2952].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2573,
2574].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2269].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2024].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1827].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1651].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1521].
colnames(FullDF_loc)=c("Location",cutoffs[1:10])
Melt:
FullDF_loc_melt=melt(FullDF_loc,id.vars = "Location",variable.name = "Cutoff", value.name = "NPas") %>% group_by(Cutoff) %>% mutate(propPAS=NPas/sum(NPas))
totplotloc=ggplot(FullDF_loc_melt,aes(x=Cutoff, y=propPAS, by=Location, fill=Location)) + geom_bar(width=.5, stat="identity") + scale_fill_brewer(palette="GnBu") + labs(title="PAS location\n Total Fraction",y="Proportion of PAS", x="Usage Filter cutoff")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_x_discrete(name="Usage Filter cutoff", breaks=c("0","0.1","0.2", "0.3", "0.4","0.5"))
Nuclear
locbycutoff_nuc=function(fraction){
nucapaMeananno_filt=nucapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(loc) %>% summarise(PerLoc=n()) %>%filter(loc!= "008559")
return(nucapaMeananno_filt$PerLoc)
}
NucFullDF_loc=as.data.frame(cbind(locations))
cutoffs=seq(from=0, to=.5, by=.05)
for (val in cutoffs)
{
NucFullDF_loc=cbind(NucFullDF_loc,val=locbycutoff_nuc(val))
}
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [5399,
5400, 5401, 5402].
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3814,
3815, 3816].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [3076,
3077].
Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2630,
2631].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2310].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2053].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1821].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1645].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1507].
Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1366].
colnames(NucFullDF_loc)=c("Location",cutoffs)
Melt:
NucFullDF_locMelt=melt(NucFullDF_loc,id.vars = "Location",variable.name = "Cutoff", value.name = "NPas") %>% group_by(Cutoff) %>% mutate(propPAS=NPas/sum(NPas))
nucplotloc=ggplot(NucFullDF_locMelt,aes(x=Cutoff, y=propPAS, by=Location, fill=Location)) + geom_bar(width = .5, stat="identity") + scale_fill_brewer(palette="GnBu") + labs(title="PAS location\n Nuclear Fraction",y="Proportion of PAS", x="Usage Filter cutoff")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_x_discrete(name="Usage Filter cutoff", breaks=c("0","0.1","0.2", "0.3", "0.4","0.5"))
Plot next to eachother
plot_grid(nucplotloc, totplotloc)
totplotloc
Version | Author | Date |
---|---|---|
0dbb65b | brimittleman | 2019-05-22 |
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_3.1.1 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_0.25.2
[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