Last updated: 2020-01-21

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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/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd c27674c brimittleman 2020-01-21 remove disc in chimp
html c211e21 brimittleman 2020-01-18 Build site.
Rmd 7a9f608 brimittleman 2020-01-18 finished benchmark
html 0c14f51 brimittleman 2020-01-17 Build site.
Rmd 11b26b4 brimittleman 2020-01-17 add benchmark res
html 9024e86 brimittleman 2020-01-17 Build site.
Rmd 6c72627 brimittleman 2020-01-17 add code to make sample APS
html 5eef3eb brimittleman 2020-01-16 Build site.
Rmd 5c24c0c brimittleman 2020-01-16 add cutoff code files 
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(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

In this analyisis I will chose the 3 sets of 6 individuals randomly from my previous study to use for a benchmark analysis.

I will need to rerun the snakefile each time.

indiv=read.table("../../apaQTL/data/MetaDataSequencing.txt", header= T, stringsAsFactors = F) %>% dplyr::select(line) %>% unique()
mkdir ../data/OverlapBenchmark

Randomly choose 3 sets:

#sample1= sample(indiv$line, 6)
#sample2= sample(indiv$line, 6)
#sample3= sample(indiv$line, 6)

#save(sample1, sample2,sample3, file = "../data/OverlapBenchmark/samples.RData")


load("../data/OverlapBenchmark/samples.RData")
sample1
[1] "NA19144" "NA18508" "NA18858" "NA19239" "NA18504" "NA19119"
sample2
[1] "NA18522" "NA18516" "NA18504" "NA19119" "NA19131" "NA19137"
sample3
[1] "NA19131" "NA18486" "NA19210" "NA19223" "NA19092" "NA19200"
mkdir ../../PAS_Sample1
mkdir ../../PAS_Sample1/code
mkdir ../../PAS_Sample1/data
mkdir ../../PAS_Sample1/data/fastq

mkdir ../../PAS_Sample2
mkdir ../../PAS_Sample2/code
mkdir ../../PAS_Sample2/data
mkdir ../../PAS_Sample2/data/fastq


mkdir ../../PAS_Sample3/
mkdir ../../PAS_Sample3/code/
mkdir ../../PAS_Sample3/data/
mkdir ../../PAS_Sample3/data/fastq

Move over these fastq files.

I will also move the necessary snakefile and run files.

Next steps:

’

mkdir ../data/cleanPeaks_anno
bedtools map -a ../data/cleanPeaks/human_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed -b  /project2/gilad/briana/genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation.sort.bed -c 4 -S -o distinct > ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed 

python chooseAnno2Bed.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed

python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.SAF

mkdir  ../data/CleanLiftedPeaks_FC/

sbatch quantLiftedPAS.sh 


###
python fixFChead_bothfrac.py ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc


python makeFileID.py ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../data/CleanLiftedPeaks_FC/HumanFileID.txt


mkdir ../data/phenotype/

python makePheno.py  ../data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc ../data/CleanLiftedPeaks_FC/HumanFileID.txt ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt

Rscript pheno2countonly.R -I ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt -O ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt

python convertNumeric.py ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt ../data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt

Pull these in, pull just the nuclear and get the mean:

Sample 1

Sample1Anno=read.table("../../PAS_Sample1/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("start1", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "PAS","chr"), sep="-")

Sample1Ind=colnames(Sample1Anno)[9:ncol(Sample1Anno)]

Sample1Usage=read.table("../../PAS_Sample1/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = Sample1Ind) %>% dplyr::select(contains("_N")) 

Sample1All=as.data.frame(cbind(cbind(Sample1Anno[,1:8], Sample1=rowMeans(Sample1Usage))))

Sample 2:

Sample2Anno=read.table("../../PAS_Sample2/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("start1", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "PAS","chr"), sep="-")

Sample2Ind=colnames(Sample2Anno)[9:ncol(Sample2Anno)]

Sample2Usage=read.table("../../PAS_Sample2/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = Sample2Ind) %>% dplyr::select(contains("_N")) 

Sample2All=as.data.frame(cbind(cbind(Sample2Anno[,1:8], Sample2=rowMeans(Sample2Usage))))

Sample 3

Sample3Anno=read.table("../../PAS_Sample3/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("start1", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "PAS","chr"), sep="-")

Sample3Ind=colnames(Sample3Anno)[9:ncol(Sample3Anno)]

Sample3Usage=read.table("../../PAS_Sample3/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = Sample3Ind) %>% dplyr::select(contains("_N")) 

Sample3All=as.data.frame(cbind(cbind(Sample3Anno[,1:8], Sample3=rowMeans(Sample3Usage))))

I need to make a bed file with these to overlap with the original PAS. I want 100 bp on each side of the end. Positive strand (actual negative strand) take

Sample1bed=Sample1All %>% mutate(PASName=paste(gene,PAS, sep="_"),newStart=ifelse(strand=="+", as.integer(start)-100, as.integer(end)-100), newEnd=ifelse(strand=="+",as.integer(start)+100, as.integer(end)+100)) %>%  dplyr::select(chr, newStart, newEnd, PASName, Sample1, strand)

#write.table(Sample1bed,"../data/OverlapBenchmark/sample1PAS.bed", col.names = F, row.names = F, sep="\t", quote = F )

Sample2bed=Sample2All %>% mutate(PASName=paste(gene,PAS, sep="_"),newStart=ifelse(strand=="+", as.integer(start)-100, as.integer(end)-100), newEnd=ifelse(strand=="+",as.integer(start)+100, as.integer(end)+100)) %>%  dplyr::select(chr, newStart, newEnd, PASName, Sample2, strand)

#write.table(Sample2bed,"../data/OverlapBenchmark/sample2PAS.bed", col.names = F, row.names = F, sep="\t", quote = F )

Sample3bed=Sample3All %>% mutate(PASName=paste(gene,PAS, sep="_"),newStart=ifelse(strand=="+", as.integer(start)-100, as.integer(end)-100), newEnd=ifelse(strand=="+",as.integer(start)+100, as.integer(end)+100)) %>%  dplyr::select(chr, newStart, newEnd, PASName, Sample3, strand)

#write.table(Sample3bed,"../data/OverlapBenchmark/sample3PAS.bed", col.names = F, row.names = F, sep="\t", quote = F )
sort -k1,1 -k2,2n ../data/OverlapBenchmark/sample1PAS.bed > ../data/OverlapBenchmark/sample1PAS_sort.bed

sort -k1,1 -k2,2n ../data/OverlapBenchmark/sample2PAS.bed > ../data/OverlapBenchmark/sample2PAS_sort.bed

sort -k1,1 -k2,2n ../data/OverlapBenchmark/sample3PAS.bed > ../data/OverlapBenchmark/sample3PAS_sort.bed

Next step is to assess the overlap.

sbatch overlapapaQTLPAS_samples.sh

Sample 1 res:

wOverlap1=read.table("../data/OverlapBenchmark/sample1PAS_sort.Intersect.bed", col.names = colnames(Sample1bed)) %>% mutate(overlap="yes")
noOverlap1=read.table("../data/OverlapBenchmark/sample1PAS_sort.Intersect.NoOverlap.bed", col.names = colnames(Sample1bed)) %>% mutate(overlap="no")

AllwOinfo1=as.data.frame(rbind(wOverlap1, noOverlap1))
nrow(AllwOinfo1)
[1] 77850
overlap1=c()
totalvec1=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
  x=AllwOinfo1 %>% filter(Sample1>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
  overlap1=c(overlap1,prop)
  totalvec1=c(totalvec1,total)
}

plot(seq_usage,overlap1,main="Sample 1", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")

Version Author Date
0c14f51 brimittleman 2020-01-17

Sample 2

wOverlap2=read.table("../data/OverlapBenchmark/sample2PAS_sort.Intersect.bed", col.names = colnames(Sample2bed)) %>% mutate(overlap="yes")
noOverlap2=read.table("../data/OverlapBenchmark/sample2PAS_sort.Intersect.NoOverlap.bed", col.names = colnames(Sample2bed)) %>% mutate(overlap="no")

AllwOinfo2=as.data.frame(rbind(wOverlap2, noOverlap2))
nrow(AllwOinfo2)
[1] 106751
overlap2=c()
totalvec2=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
  x=AllwOinfo2 %>% filter(Sample2>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
  overlap2=c(overlap2,prop)
  totalve2c=c(totalvec2,total)
}

plot(seq_usage,overlap2,main="Sample 2", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")

Version Author Date
0c14f51 brimittleman 2020-01-17

Sample 3

wOverlap3=read.table("../data/OverlapBenchmark/sample3PAS_sort.Intersect.bed", col.names = colnames(Sample3bed)) %>% mutate(overlap="yes")
noOverlap3=read.table("../data/OverlapBenchmark/sample3PAS_sort.Intersect.NoOverlap.bed", col.names = colnames(Sample3bed)) %>% mutate(overlap="no")

AllwOinfo3=as.data.frame(rbind(wOverlap3, noOverlap3))
nrow(AllwOinfo3)
[1] 96769
overlap3=c()
totalvec3=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
  x=AllwOinfo3 %>% filter(Sample3>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
  overlap3=c(overlap3,prop)
  totalvec3=c(totalvec3,total)
}

plot(seq_usage,overlap3,main="Sample 2", ylab="Percent Overlap", xlab="Usage Cutoff")
abline(v=.05,col="red")
abline(v=.1,col="blue")

Version Author Date
0c14f51 brimittleman 2020-01-17

Plot with all:

allSamp=as.data.frame(cbind(seq_usage,overlap1,overlap2,overlap3))
actualres= read.table("../data/CompapaQTLpas/ExtendedResoverlap.txt", header = T)

SampandRes=allSamp %>% inner_join(actualres,by="seq_usage") 

colnames(SampandRes)= c("Cutoff","Sample1", "Sample2", "Sample3", "CompPAS")

SampandRes_gather=SampandRes %>% gather(key="set", value="overlap",-Cutoff)

Plot:

bench=ggplot(SampandRes_gather,aes(x=Cutoff,y=overlap, by=set, col=set))+ geom_line() + labs(title="Benchmark results with 3 sets of 6 samples") + geom_vline(xintercept=.05,col="red")+ geom_vline(xintercept=.1,col="blue")

bench

Version Author Date
c211e21 brimittleman 2020-01-18
0c14f51 brimittleman 2020-01-17

I will filter this to only genes passing the filter I established in the previous:

PassingGenes=read.table("../data/OverlapBenchmark/genesPassingCuttoff.txt", header = T, stringsAsFactors = F)
AllwOinfo1_filt=AllwOinfo1 %>% separate(PASName, into=c("gene", "PAS"), sep="_")%>% filter(gene %in% PassingGenes$genes)


AllwOinfo2_filt=AllwOinfo2 %>% separate(PASName, into=c("gene", "PAS"), sep="_") %>% filter(gene %in% PassingGenes$genes)
AllwOinfo3_filt=AllwOinfo3 %>% separate(PASName, into=c("gene", "PAS"), sep="_") %>% filter(gene %in% PassingGenes$genes)

Actual results:

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) 
metaDataPAS=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, gene)


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)) %>% inner_join(metaDataPAS,by="PAS") %>% filter(gene %in% PassingGenes$genes)
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
nrow(AllwOinfoExt)
[1] 36802
overlapFilt1=c()
seq_usage=seq(0, .95, .01)
for (i in seq_usage){
  x=AllwOinfo1_filt %>% filter(Sample1>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
  overlapFilt1=c(overlapFilt1,prop)
}

overlapFilt2=c()
for (i in seq_usage){
  x=AllwOinfo2_filt %>% filter(Sample2>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
  overlapFilt2=c(overlapFilt2,prop)
}

overlapFilt3=c()
for (i in seq_usage){
  x=AllwOinfo3_filt %>% filter(Sample3>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
  overlapFilt3=c(overlapFilt3,prop)
}

overlapFiltActual=c()
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
  overlapFiltActual=c(overlapFiltActual,prop)
}

FilteredRes=as.data.frame(cbind(seq_usage,overlapFilt1,overlapFilt2,overlapFilt3,overlapFiltActual))
colnames(FilteredRes)= c("Cutoff","Sample1", "Sample2", "Sample3", "CompPAS")

FilteredRes_gather=FilteredRes %>% gather(key="set", value="overlap",-Cutoff)
filteredbench=ggplot(FilteredRes_gather,aes(x=Cutoff,y=overlap, by=set, col=set))+ geom_line() + labs(title="Benchmark results with 3 sets of 6 samples\n filtered gene expresssion") + geom_vline(xintercept=.05,col="red")+ geom_vline(xintercept=.1,col="blue")

filteredbench

Version Author Date
c211e21 brimittleman 2020-01-18

Last step is to remove the PAS that were discovered originally in chimp.

PASdisc=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% filter(disc!="Chimp")

Filter and rerun loop for percent overlap. Only the actual need to be updated for this.

AllwOinfoExt_noChimp =AllwOinfoExt %>% filter(PAS %in% PASdisc$PAS)

Rerun:

overlapFiltActualnoChimp=c()
for (i in seq_usage){
  x=AllwOinfoExt_noChimp %>% 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
  overlapFiltActualnoChimp=c(overlapFiltActualnoChimp,prop)
}

FilteredResNochimp=as.data.frame(cbind(seq_usage,overlapFilt1,overlapFilt2,overlapFilt3,overlapFiltActualnoChimp))
colnames(FilteredResNochimp)= c("Cutoff","Sample1", "Sample2", "Sample3", "CompPAS")  

FilteredResNochimp_gather=FilteredResNochimp %>% gather(key="set", value="overlap",-Cutoff)
filteredbencnoChimop=ggplot(FilteredResNochimp_gather,aes(x=Cutoff,y=overlap, by=set, col=set))+ geom_line() + labs(title="Benchmark results with 3 sets of 6 samples\n filtered gene expresssion, remove discovered in Chimp") + geom_vline(xintercept=.05,col="red")+ geom_vline(xintercept=.1,col="blue")

filteredbencnoChimop

Plot together:

plot_grid(bench,filteredbench,filteredbencnoChimop, nrow=1)

Looks like using a 10% usage and this expression cuttoff is pretty good.


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   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] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     workflowr_1.5.0  cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1