Last updated: 2019-09-15

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/apaQTLsnake.err
    Deleted:    code/test.txt
    Deleted:    reads_graphs.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.

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File Version Author Date Message
Rmd 7c4debc brimittleman 2019-09-15 add inclusive PAS analysis 
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(qvalue)

I want to call QTLs with all of the PAS to check for specificity.

I can filter the phenotypes in ../data/phenotype by the PAS in

APApeak_Phenotype_GeneLocAnno.Total.fc

allPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed", header=F,col.names=c("chr","start", "end", "ID", "score","strand"),stringsAsFactors = F) %>% separate(ID, into = c("PASnum", "geneID"),sep=":") %>% mutate(PAS_ID=paste("peak", PASnum, sep=""))

TotalPheno:

totalPheo=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc",header = T,stringsAsFactors = F) 

totalPheo_peaknum= totalPheo%>% separate(chrom, into=c("chr","start","end", "ID"),sep=":") %>% separate(ID, into=c("gene", "loc", "strand", "PAS_ID"), sep="_") %>% semi_join(allPAS, by="PAS_ID") %>% mutate(ID=paste(gene,loc,strand,PAS_ID,sep="_"), chrom= paste(chr,start,end,ID,sep=":")) %>% select(chrom)
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].
totalPheo_filt=totalPheo %>% semi_join(totalPheo_peaknum,by="chrom")

NuclearPheno:

NuclearPheno=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.fc",header = T,stringsAsFactors = F) 

NuclearPheno_peaknum= NuclearPheno%>% separate(chrom, into=c("chr","start","end", "ID"),sep=":") %>% separate(ID, into=c("gene", "loc", "strand", "PAS_ID"), sep="_") %>% semi_join(allPAS, by="PAS_ID") %>% mutate(ID=paste(gene,loc,strand,PAS_ID,sep="_"), chrom= paste(chr,start,end,ID,sep=":")) %>% select(chrom)
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].
NuclearPheno_filt=NuclearPheno %>% semi_join(NuclearPheno,by="chrom")

write out:

mkdir ../data/phenotype_inclusivePAS
mkdir ../data/apaQTLNominal_inclusive
write.table(NuclearPheno_filt, row.names = F, col.names = T, "../data/phenotype_inclusivePAS/APApeak_Phenotype_GeneLocAnno.Nuclear.fc", quote=F)

write.table(totalPheo_filt, row.names = F, col.names = T, "../data/phenotype_inclusivePAS/APApeak_Phenotype_GeneLocAnno.Total.fc", quote=F)
#!/bin/bash
#python2 

gzip APApeak_Phenotype_GeneLocAnno.Total.fc
gzip APApeak_Phenotype_GeneLocAnno.Nuclear.fc

python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Total.fc.gz
python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz

sh APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz_prepare.sh
sh APApeak_Phenotype_GeneLocAnno.Total.fc.gz_prepare.sh

head -n5 APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.4PCs
head -n5 APApeak_Phenotype_GeneLocAnno.Total.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Total.fc.gz.4PCs

#code dir
sbatch apaQTL_nominalInclusive.sh

#concatinate res: 

cat ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Total.fc.gz.qqnorm_chr* > ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Total.fc.gz.qqnorm_allChr.txt
cat ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.qqnorm_chr* > ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.qqnorm_allChr.txt


mkdir ../data/QTLoverlap_inclusive/

python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.qqnorm_allChr.txt ../data/QTLoverlap_inclusive/TotalQTLinNuclearNominal_inc.txt 


python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Total.fc.gz.qqnorm_allChr.txt ../data/QTLoverlap_inclusive/NuclearQTLinTotalNominal_inc.txt 
totAPAinNuc=read.table("../data/QTLoverlap_inclusive/TotalQTLinNuclearNominal_inc.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_tot=pi0est(totAPAinNuc$pval, pi0.method = "bootstrap")

nucAPAinTot=read.table("../data/QTLoverlap_inclusive/NuclearQTLinTotalNominal_inc.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_nuc=pi0est(nucAPAinTot$pval, pi0.method = "bootstrap")


par(mfrow=c(1,2))
hist(totAPAinNuc$pval, xlab="Nuclear Pvalue", main="Significant Total APA QTLs \n Nuclear")
text(.8,300, paste("pi_1=", round((1-qval_tot$pi0), digit=3), sep=" "))
hist(nucAPAinTot$pval, xlab="Total Pvalue", main="Significant Nuclear APA QTLs \n Total")
text(.8,350, paste("pi_1=", round((1-qval_nuc$pi0), digit=3), sep=" "))

totAPAinNuc_notsig=totAPAinNuc %>% filter(pval>.05)
nrow(totAPAinNuc_notsig)
[1] 97
nucAPAinTot_notsig=nucAPAinTot %>% filter(pval>.05)
nrow(nucAPAinTot_notsig)
[1] 151
prop.test(x=c(97,151),n=c(443,603), alternative="less")

    2-sample test for equality of proportions with continuity
    correction

data:  c(97, 151) out of c(443, 603)
X-squared = 1.2283, df = 1, p-value = 0.1339
alternative hypothesis: less
95 percent confidence interval:
 -1.00000000  0.01394063
sample estimates:
   prop 1    prop 2 
0.2189616 0.2504146 

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] qvalue_2.14.0   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] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        workflowr_1.4.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] reshape2_1.4.3   modelr_0.1.2     magrittr_1.5     whisker_0.3-2   
[41] splines_3.5.1    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[45] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4