Last updated: 2019-08-30

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PAS_graphs.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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File Version Author Date Message
html 1d8fbcb brimittleman 2019-08-29 Build site.
Rmd a6705c8 brimittleman 2019-08-29 add effect size corr for nuc specific
html 2fb467b brimittleman 2019-07-07 Build site.
Rmd bd5f228 brimittleman 2019-07-07 add nuc spec eqtl res

I want to make a qq plot for the eQTL results then seperate the values by genes with a nuclear specific apaQTL.

library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started

First i need to run the permutations on the eQTL data to get the top snp gene associations.

sbatch EandPqtl_perm.sh
cat ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.chunk* > ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.AllNomRes.txt

python changePermQTLres2geneName.py
permeqtl=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.AllNomRes.GeneName.txt", col.names = c("gene", "nvar","shape1", "shape2", "dummy", "RSID", "dist","nomPval","slope","ppval", "bpval"),stringsAsFactors = F)

permeqtl$bh=p.adjust(permeqtl$bpval, method="fdr")
qqplot(-log10(runif(nrow(permeqtl))), -log10(permeqtl$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="eQTL")
abline(0,1)

Version Author Date
2fb467b brimittleman 2019-07-07

Seperate for genes with a nuclear specific apaQTL

nuclear specific (only tested in nuclear) are in ../data/QTLoverlap_nonNorm/NuclearSpecQTLinNuclearNominal_nonNorm.txt

nuclear specific not sig in total:

nucSpecgene=read.table("../data/QTLoverlap_nonNorm/NuclearSpecQTLinNuclearNominal_nonNorm.txt",header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "Originalslope")) %>% separate(peakID,into=c("gene","loc", "strand", "PAS"),sep="_") %>% select(gene) %>% unique()

nucAPAinTot=read.table("../data/QTLoverlap/NuclearQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% separate(peakID,into=c("chr", "start", "end", "geneID"),sep=":" ) %>% separate(geneID, into=c("gene", "loc", "strand", "peakNum"), sep="_")
nucAPAinTot_NOTSIG=nucAPAinTot %>% filter(pval>.05) %>% select(gene) %>% unique()

allNucspec=nucAPAinTot_NOTSIG %>% full_join(nucSpecgene,by="gene")

There are 172 genes in this set.

permeqtl_nucSpec= permeqtl  %>% filter(gene %in%allNucspec$gene )

permeqtl_notnucSpec= permeqtl  %>% anti_join(permeqtl_nucSpec,by = c("gene", "nvar", "shape1", "shape2", "dummy", "RSID", "dist", "nomPval", "slope", "ppval", "bpval", "bh"))
qqplot(-log10(runif(nrow(permeqtl_notnucSpec))), -log10(permeqtl_notnucSpec$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="eQTL")
points(sort(-log10(runif(nrow(permeqtl_nucSpec)))), sort(-log10(permeqtl_nucSpec$bpval)),col= alpha("Red"))

abline(0,1)
legend("topleft", legend=c("Nuclear specific apaQTL genes", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

Version Author Date
1d8fbcb brimittleman 2019-08-29
2fb467b brimittleman 2019-07-07
wilcox.test(permeqtl_notnucSpec$ppval,permeqtl_nucSpec$ppval)

    Wilcoxon rank sum test with continuity correction

data:  permeqtl_notnucSpec$ppval and permeqtl_nucSpec$ppval
W = 194420, p-value = 0.1278
alternative hypothesis: true location shift is not equal to 0

Compare effect sizes:

#allNucspec is the nuclear specific gene, will pull these genes out 

nuclearQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs_5fdr.txt", header = T,stringsAsFactors = F) %>% rename("gene"= Gene) %>% semi_join(allNucspec, by="gene")

write.table(nuclearQTLs, file="../data/apaQTLs/NuclearSpecificAPAqtls.txt", col.names = T, row.names = F, quote = F, sep="\t")
python nucSpecQTLineData.py

Join these by snp and gene.

nomNamesE=c("gene", "sid",'dist', 'pval', 'Eslope' )
eQTLforNucspec=read.table("../data/eQTLs/NuclearSPecificAPAqtlsinEqtl.txt",stringsAsFactors = F, col.names = nomNamesE)

bothNucspecandE=eQTLforNucspec %>% inner_join(nuclearQTLs, by=c("gene", "sid"))

summary(lm(bothNucspecandE$slope~bothNucspecandE$Eslope))

Call:
lm(formula = bothNucspecandE$slope ~ bothNucspecandE$Eslope)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8866 -1.0886  0.4571  0.7981  2.2386 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             0.38684    0.07066   5.475 1.15e-07 ***
bothNucspecandE$Eslope -0.11181    0.23729  -0.471    0.638    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.071 on 228 degrees of freedom
Multiple R-squared:  0.0009729, Adjusted R-squared:  -0.003409 
F-statistic: 0.222 on 1 and 228 DF,  p-value: 0.6379
cor.test(bothNucspecandE$slope,bothNucspecandE$Eslope, alternative="less")

    Pearson's product-moment correlation

data:  bothNucspecandE$slope and bothNucspecandE$Eslope
t = -0.4712, df = 228, p-value = 0.319
alternative hypothesis: true correlation is less than 0
95 percent confidence interval:
 -1.00000000  0.07781426
sample estimates:
        cor 
-0.03119076 
ggplot(bothNucspecandE, aes(x=Eslope, y=slope)) + geom_point() + geom_smooth(method="lm") + labs(x="eQTL effect size", y="apaQTL effect size", title="Effect size in eQTL for nuclear specific apaQTL")

Version Author Date
1d8fbcb brimittleman 2019-08-29

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] workflowr_1.4.0 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_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4