Last updated: 2019-07-07

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/DiffIsoAnalysis.Rmd
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
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    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    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
    Deleted:    code/test.txt

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 f85f6d0 brimittleman 2019-07-07 add ptt eqtls

This analysis will be similar to the nuclear specific in egenes analysis. I want to ask if a gene is more likely to be associated with expression if it has a pttQTL in total or nuclear.

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(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
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)
totptt=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% separate(pid,into = c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
nucptt=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% separate(pid,into = c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
permeqtl_tot= permeqtl  %>% filter(gene %in%totptt$gene )

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

abline(0,1)
legend("topleft", legend=c("Total ptt gene", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

permeqtl_nuc= permeqtl  %>% filter(gene %in%nucptt$gene )

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

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

In either:

permeqtl_either= permeqtl  %>% filter(gene %in%totptt$gene |gene %in%nucptt$gene )

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

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


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 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4