Last updated: 2019-09-17

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.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/DistPAS2Sig.py
    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.

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 f8cb7b8 brimittleman 2019-09-17 move inclusive, get numbers for paper
html 2bc4187 brimittleman 2019-09-15 Build site.
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=" "))

Version Author Date
2bc4187 brimittleman 2019-09-15 
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

location for specific:

totAPAinNuc_notsig_loc=totAPAinNuc_notsig %>% separate(peakID, into=c("chr","start","end", "pasid"), sep=":") %>% separate(pasid, into=c("gene","loc","strand","pas"), sep="_") %>% group_by(loc) %>% summarize(n=n())


totAPAinNuc_notsig_loc
# A tibble: 5 x 2
  loc        n
  <chr>  <int>
1 cds       11
2 end        6
3 intron    25
4 utr3      54
5 utr5       1
25/nrow(totAPAinNuc_notsig)
[1] 0.257732
nucAPAinTot_notsig_loc=nucAPAinTot_notsig %>% separate(peakID, into=c("chr","start","end", "pasid"), sep=":") %>% separate(pasid, into=c("gene","loc","strand","pas"), sep="_") %>% group_by(loc) %>% summarize(n=n())


nucAPAinTot_notsig_loc
# A tibble: 5 x 2
  loc        n
  <chr>  <int>
1 cds        7
2 end       22
3 intron    50
4 utr3      67
5 utr5       5
50/nrow(nucAPAinTot_notsig)
[1] 0.3311258

Look at nuclear specific in eQTL:

nucAPAinTot_notsig_small=nucAPAinTot_notsig %>% separate(peakID, into=c("chr","start","end", "pasid"), sep=":") %>% separate(pasid, into=c("gene","loc","strand","pas"), sep="_") %>% select(gene, pas, snp)

write.table(nucAPAinTot_notsig_small, "../data/QTLoverlap_inclusive/NuclearSpecApaQTLinclusive.txt", col.names=T, row.names=F, quote=F)

Test these is edata:

python nucspecinE.py
nucspecinE=read.table("../data/QTLoverlap_inclusive/NuclearSpecApaQTLinclusive_withE.txt", stringsAsFactors =F, col.names=c("peakID", 'snp','dist', 'pval', 'slope'))

sig=nucspecinE %>% filter(pval<.05)

nrow(sig)
[1] 13

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] tidyselect_0.2.5 reshape2_1.4.3   splines_3.5.1    haven_1.1.2     
 [5] lattice_0.20-38  colorspace_1.3-2 generics_0.0.2   htmltools_0.3.6 
 [9] yaml_2.2.0       utf8_1.1.4       rlang_0.4.0      pillar_1.3.1    
[13] glue_1.3.0       withr_2.1.2      modelr_0.1.2     readxl_1.1.0    
[17] plyr_1.8.4       munsell_0.5.0    gtable_0.2.0     workflowr_1.4.0 
[21] cellranger_1.1.0 rvest_0.3.2      evaluate_0.12    knitr_1.20      
[25] fansi_0.4.0      highr_0.7        broom_0.5.1      Rcpp_1.0.2      
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        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      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10  
[49] httr_1.3.1       rstudioapi_0.10  R6_2.3.0         nlme_3.1-137    
[53] git2r_0.25.2     compiler_3.5.1