Use apaQTL Individuals to establish cutoffs

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/establishCutoffs.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
html	42c66a9	brimittleman	2020-01-21	Build site.
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:

assign to genes and get pheno ratio (https://brimittleman.github.io/Comparative_APA/annotatePAS.html) - run code in all 3 locations

’

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
42c66a9	brimittleman	2020-01-21
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

Version	Author	Date
42c66a9	brimittleman	2020-01-21

Plot together:

plot_grid(bench,filteredbench,filteredbencnoChimop)

Version	Author	Date
42c66a9	brimittleman	2020-01-21

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

Use apaQTL Individuals to establish cutoffs

Briana Mittleman

1/16/2020