Last updated: 2020-01-15

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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/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File Version Author Date Message
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  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()
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")

Version Author Date
9fb15eb brimittleman 2019-12-17
cf0de17 brimittleman 2019-10-09
4ea2576 brimittleman 2019-10-04 
ggplot(AllwOinfo, aes(x=overlap, y=Human, fill=overlap)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method="t.test")

Version Author Date
9fb15eb brimittleman 2019-12-17
cf0de17 brimittleman 2019-10-09
4ea2576 brimittleman 2019-10-04

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")

Version Author Date
9fb15eb brimittleman 2019-12-17
3d1bad5 brimittleman 2019-10-14

Still a not high overlap, but it is goood to see those that overlap are the higher used.

Expression Filter

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")

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")

Look at some of these in IGV:

It look like some of them are off by just a few basepairs. To be more liberal with this analysis, I will increase the size of the apaQTLs by 50 on each side.

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")

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")

nrow(AllwOinfoExt_gene_filt)
[1] 35607

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