Last updated: 2020-01-17
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
Modified: analysis/OppositeMap.Rmd
Modified: analysis/annotationInfo.Rmd
Modified: analysis/correlationPhenos.Rmd
Modified: analysis/investigatePantro5.Rmd
Modified: analysis/multiMap.Rmd
Modified: analysis/speciesSpecific.Rmd
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 11b26b4 | brimittleman | 2020-01-17 | add benchmark res |
html | 3a96e17 | brimittleman | 2020-01-15 | Build site. |
Rmd | 3bcd5af | brimittleman | 2020-01-15 | add usage cuttoff plots |
html | 7bac2c5 | brimittleman | 2020-01-15 | Build site. |
Rmd | f405585 | brimittleman | 2020-01-15 | add boxplot for extended |
html | 9e0680a | brimittleman | 2020-01-15 | Build site. |
Rmd | 65a4e09 | brimittleman | 2020-01-15 | filter overlap with expression |
html | 9fb15eb | brimittleman | 2019-12-17 | Build site. |
Rmd | d632096 | brimittleman | 2019-12-17 | update comp 3 apapas |
html | 3d1bad5 | brimittleman | 2019-10-14 | Build site. |
Rmd | 63fddf4 | brimittleman | 2019-10-14 | add count comparison |
html | cf0de17 | brimittleman | 2019-10-09 | Build site. |
Rmd | 190a655 | brimittleman | 2019-10-09 | small changes 9.9 |
html | 4ea2576 | brimittleman | 2019-10-04 | Build site. |
Rmd | 920abac | brimittleman | 2019-10-04 | add comaprison to old PAS |
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble 2.1.1 ✔ purrr 0.3.2
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✔ readr 1.3.1 ✔ stringr 1.3.1
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── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
In this analysis I will compare the PAS found at 5% in the human data in this project with the apaQTL project.
Take the 5 perc PAS from the annotatePAS analysis and filter those with scores > .05 in human. I also want to remove PAS not in chr 1-22.
chroms=c('chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr1', 'chr2', 'chr20', 'chr21', 'chr22', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7','chr8', 'chr9')
compAPAPAS=read.table("../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed", header = T, stringsAsFactors = F) %>% filter(Human>=0.05, chr %in% chroms)
I can write this bed out and use bedtools intersect.
mkdir ../data/CompapaQTLpas
write.table(compAPAPAS,"../data/CompapaQTLpas/CompAPA_PAS_5percHuman.bed", col.names = F, row.names = F, quote = F, sep="\t")
sort -k1,1 -k2,2n ../data/CompapaQTLpas/CompAPA_PAS_5percHuman.bed > ../data/CompapaQTLpas/CompAPA_PAS_5percHuman.sort.bed
sbatch overlapapaQTLPAS.sh
Compare the ones with overlaps to those without:
wOverlap=read.table("../data/CompapaQTLpas/PAS_5percHuman.sort.Intersect.bed", col.names = colnames(compAPAPAS)) %>% mutate(overlap="yes")
noOverlap=read.table("../data/CompapaQTLpas/PAS_5percHuman.sort.Intersect.NoOverlap.bed", col.names = colnames(compAPAPAS)) %>% mutate(overlap="no")
AllwOinfo=as.data.frame(rbind(wOverlap, noOverlap))
nrow(AllwOinfo)
[1] 59783
ggplot(AllwOinfo, aes(x=overlap,fill=overlap)) +geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2")
ggplot(AllwOinfo, aes(x=overlap, y=Human, fill=overlap)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method="t.test")
Look at actual counts to see if this is a noise problem:
humanCounts=read.table("../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc",header = T, stringsAsFactors = F) %>% dplyr::select(-Chr, -Start,-End, -Strand, -Length) %>% tidyr::separate(Geneid, sep = ":", into = c("disc", "PAS", "chr", "start", "end", "strand", "gene"))
humanCounts_mean=rowMeans(humanCounts[,8:19])
HumanMeanAnno= as.data.frame(cbind(PAS=humanCounts$PAS, meanCount=humanCounts_mean)) %>% inner_join(AllwOinfo, by= "PAS")
Warning: Column `PAS` joining factors with different levels, coercing to
character vector
HumanMeanAnno$overlap=as.factor(HumanMeanAnno$overlap)
HumanMeanAnno$meanCount=as.integer(as.character(HumanMeanAnno$meanCount))
ggplot(HumanMeanAnno, aes(x=overlap, y=log10(meanCount +1),fill=overlap)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2")+ stat_compare_means(method="t.test") + labs(title="HC PAS counts by overlap with apaQTL PAS")
Get the percent overlap by usage filter:
overlap=c()
totalvec=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
x=AllwOinfo %>% filter(Human>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlap=c(overlap,prop)
totalvec=c(totalvec,total)
}
plot(seq_usage,overlap,main="Overlap with apaQTL PAS by human average usage", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")
Version | Author | Date |
---|---|---|
3a96e17 | brimittleman | 2020-01-15 |
plot(seq_usage,totalvec,main="Number of PAS",ylab="Number of PAS",xlab="Usage Cutoff" )
abline(v=.05,col="red")
abline(v=.1,col="blue")
Extended:
apaQTLPAS=read.table("../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted.sorted.bed",stringsAsFactors = F,col.names = c("chr","start", "end", "PAS", "score","strand"))
apaQTLPAS_ext= apaQTLPAS %>% mutate(start_new=start-50, end_new=end+50) %>% dplyr::select(chr,start_new, end_new, PAS, score, strand)
write.table(apaQTLPAS_ext,"../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted_extended.bed",col.names = F, row.names = F, quote = F, sep="\t")
sort -k1,1 -k2,2n ../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted_extended.bed > ../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted_extended.sort.bed
sbatch overlapapaQTLPAS_extended.sh
wOverlapExt=read.table("../data/CompapaQTLpas/PAS_5percHuman.sort.Intersect_ext.bed", col.names = colnames(compAPAPAS)) %>% mutate(overlap="yes")
noOverlapExt=read.table("../data/CompapaQTLpas/PAS_5percHuman.sort.Intersect.NoOverlap_ext.bed", col.names = colnames(compAPAPAS)) %>% mutate(overlap="no")
AllwOinfoExt=as.data.frame(rbind(wOverlapExt, noOverlapExt))
nrow(AllwOinfoExt)
[1] 60307
ggplot(AllwOinfoExt, aes(x=overlap,fill=overlap)) +geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2")
Version | Author | Date |
---|---|---|
3a96e17 | brimittleman | 2020-01-15 |
overlap=c()
totalvec=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
x=AllwOinfoExt %>% filter(Human>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
overlap=c(overlap,prop)
if (i==.05 | i==.1){
print(prop)
print(i)
}
totalvec=c(totalvec,total)
}
[1] 0.4646119
[1] 0.05
[1] 0.5581395
[1] 0.1
plot(seq_usage,overlap,main="Overlap with apaQTL Extended PAS by human average usage", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")
extendedres=as.data.frame(cbind(seq_usage,overlap))
write.table(extendedres, "../data/CompapaQTLpas/ExtendedResoverlap.txt", col.names = T, row.names = F, quote = F)
plot(seq_usage,totalvec,main="Number of PAS by human by usage",ylab="Number of PAS",xlab="Usage Cutoff" )
abline(v=.05,col="red")
abline(v=.1,col="blue")
Filter down to those with higher expression:
I want to only look at the genes that passed the expression filter to test for DE.
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID,Gene.name)
DEtested=read.table("../data/DiffExpression/DE_Testedgenes.txt", col.names = c("Gene_stable_ID"), stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID")
Join gene names to the intersections:
PASMeta=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt",header = T,stringsAsFactors = F) %>% dplyr::select(PAS, gene, chr, start,end,disc)
AllwOinfo_gene=AllwOinfo %>% inner_join(PASMeta,by=c("PAS", "chr","start", "end"))
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
Warning: Column `chr` joining factor and character vector, coercing into
character vector
AllwOinfo_gene_filt=AllwOinfo_gene %>% filter(gene %in% DEtested$Gene.name )
nrow(AllwOinfo_gene_filt)
[1] 35191
Now plot:
ggplot(AllwOinfo_gene_filt, aes(x=overlap,fill=overlap)) +geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2") + labs(title="Overlap with apaQTL PAS after filtering low expressed genes", y="percentage")
Version | Author | Date |
---|---|---|
3a96e17 | brimittleman | 2020-01-15 |
Remove those identified in chimp:
AllwOinfo_gene_filt_humanident=AllwOinfo_gene_filt %>% filter(disc != "Chimp")
nrow(AllwOinfo_gene_filt_humanident)
[1] 32506
ggplot(AllwOinfo_gene_filt_humanident, aes(x=overlap,fill=overlap)) +geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2") + labs(title="Overlap with apaQTL PAS after filtering low expressed genes\n and identified in chimp", y="percentage")
Version | Author | Date |
---|---|---|
3a96e17 | brimittleman | 2020-01-15 |
AllwOinfoExt_gene=AllwOinfoExt %>% inner_join(PASMeta,by=c("PAS", "chr","start", "end"))
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
Warning: Column `chr` joining factor and character vector, coercing into
character vector
AllwOinfoExt_gene_filt=AllwOinfoExt_gene %>% filter(gene %in% DEtested$Gene.name )
ggplot(AllwOinfoExt_gene_filt, aes(x=overlap,fill=overlap)) +geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2") + labs(title="Overlap with apaQTL PAS after filtering low expressed genes \n extended PAS", y="percentage")
Version | Author | Date |
---|---|---|
3a96e17 | brimittleman | 2020-01-15 |
nrow(AllwOinfoExt_gene_filt)
[1] 35607
ggplot(AllwOinfoExt_gene_filt,aes(x=overlap,y=Human,fill=overlap))+ geom_boxplot() + stat_compare_means(method="t.test") + scale_fill_brewer(palette = "Dark2")
Get the percent overlap by usage filter:
overlap=c()
totalvec=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
x=AllwOinfoExt_gene_filt %>% filter(Human>i) %>% group_by(overlap) %>% summarise(n=n())
yes=as.numeric(x[2,2])
total=as.numeric(x[2,2])+ as.numeric(x[1,2])
prop=yes/total
if (i==.05 | i==.1){
print(prop)
print(i)
}
overlap=c(overlap,prop)
totalvec=c(totalvec,total)
}
[1] 0.6058321
[1] 0.05
[1] 0.7593642
[1] 0.1
plot(seq_usage,overlap,main="Overlap with apaQTL extended PAS by human \naverage usage filtered by gene expression", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")
Version | Author | Date |
---|---|---|
3a96e17 | brimittleman | 2020-01-15 |
plot(seq_usage,totalvec,main="Number of PAS by human \naverage usage filtered by gene expression",ylab="Number of PAS",xlab="Usage Cutoff" )
abline(v=.05,col="red")
abline(v=.1,col="blue")
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] reshape2_1.4.3 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] tidyverse_1.2.1 ggpubr_0.2 magrittr_1.5 ggplot2_3.1.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38
[4] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6
[7] yaml_2.2.0 rlang_0.4.0 later_0.7.5
[10] pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] RColorBrewer_1.1-2 modelr_0.1.2 readxl_1.1.0
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] workflowr_1.5.0 cellranger_1.1.0 rvest_0.3.2
[22] evaluate_0.12 labeling_0.3 knitr_1.20
[25] httpuv_1.4.5 broom_0.5.1 Rcpp_1.0.2
[28] promises_1.0.1 scales_1.0.0 backports_1.1.2
[31] jsonlite_1.6 fs_1.3.1 hms_0.4.2
[34] digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1
[40] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[43] pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4
[46] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137
[52] git2r_0.26.1 compiler_3.5.1