Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    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
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    Modified:   code/config.yaml
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    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
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    Modified:   code/snakemakePAS.batch
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    Modified:   docs/figure/DiffIsoAnalysis.Rmd/figure1Emain-1.pdf
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    Modified:   docs/figure/propeQTLs_explained.Rmd/figure3B-1.pdf
    Deleted:    reads_graphs.Rmd

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html cd60f50 brimittleman 2019-06-20 Build site.
Rmd 6df08b6 brimittleman 2019-06-20 change analysis to include not tested in total as nuc spec
html 743c9c6 brimittleman 2019-06-17 Build site.
Rmd bc3ddae brimittleman 2019-06-17 add effect size nuc spec

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

I am interested in seeing if the nuclear specific apaQTL snps have higher eQTL effect sizes than the shared apaQTLs. This comes from my expectation that the nuclear specific apaQTLs are more likely to be eQTLs.

I am using the nuclear specific apa QTLs including those that are not tested in total from this analysis.

python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc.txt RNA ../data/NucSpeceQTLeffect/NuclearSpecficApaQTL_eQTLRes.txt
python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/SharedApaQTL_nottestinc.txt RNA ../data/NucSpeceQTLeffect/SharedApaQTL_eQTLRes.txt

Evaluate results:

eqtl4nucqtl=read.table("../data/NucSpeceQTLeffect/NuclearSpecficApaQTL_eQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F) %>% mutate(set="Nuclear")

eqtl4sharedqtl=read.table("../data/NucSpeceQTLeffect/SharedApaQTL_eQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F)%>% mutate(set="Shared")


eQTLsboth=eqtl4nucqtl %>% bind_rows(eqtl4sharedqtl)

Plot:

ggplot(eQTLsboth,aes(x=set, y=abs(effect)))+ geom_boxplot()

Version Author Date
cd60f50 brimittleman 2019-06-20
ggplot(eQTLsboth,aes(x=set, y=pval))+ geom_boxplot()+ geom_jitter() 

Version Author Date
cd60f50 brimittleman 2019-06-20

No effect. What if we jsut look at nominally sig snps.

eQTLsbothSig=eQTLsboth %>% filter(pval<.05)

ggplot(eQTLsbothSig,aes(x=set, y=abs(effect),fill=set))+ geom_boxplot() + geom_jitter() 

Version Author Date
cd60f50 brimittleman 2019-06-20
t.test(abs(effect) ~ set, data = eQTLsbothSig, alternative="greater")

    Welch Two Sample t-test

data:  abs(effect) by set
t = 0.25731, df = 28.521, p-value = 0.3994
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
 -0.1004697        Inf
sample estimates:
mean in group Nuclear  mean in group Shared 
            0.5655281             0.5476097 
python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc.txt Protein ../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc_pQTLRes.txt
python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/SharedApaQTL_nottestinc.txt Protein ../data/NucSpeceQTLeffect/SharedApaQTLnottestedinc_pQTLRes.txt
pqtl4nucqtl=read.table("../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc_pQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F) %>% mutate(set="Nuclear")

pqtl4sharedqtl=read.table("../data/NucSpeceQTLeffect/SharedApaQTLnottestedinc_pQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F)%>% mutate(set="Shared")


pQTLsboth=pqtl4nucqtl %>% bind_rows(pqtl4sharedqtl)
ggplot(pQTLsboth,aes(x=set, y=abs(effect)))+ geom_boxplot()

Version Author Date
cd60f50 brimittleman 2019-06-20
ggplot(pQTLsboth,aes(x=set, y=pval))+ geom_boxplot()+ geom_jitter() 

Version Author Date
cd60f50 brimittleman 2019-06-20
pQTLsbothSig=pQTLsboth %>% filter(pval<.05)

ggplot(pQTLsbothSig,aes(x=set, y=abs(effect),fill=set))+ geom_boxplot() + geom_jitter() 

Version Author Date
cd60f50 brimittleman 2019-06-20

Not significant.


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