Last updated: 2020-01-18

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

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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 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
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, filtered genes") + geom_vline(xintercept=.05,col="red")+ geom_vline(xintercept=.1,col="blue")

filteredbench

Plot together:

plot_grid(bench,filteredbench)


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