Total and Nuclear QTL sharing
Briana Mittleman
5/20/2019
Last updated: 2019-06-13
Checks: 6 0
Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/index.Rmd
Modified: analysis/motifDisruption.Rmd
Modified: analysis/nascenttranscription.Rmd
Modified: analysis/nucintronicanalysis.Rmd
Modified: analysis/overlapapaqtlsandeqtls.Rmd
Modified: analysis/rna_netseq_h3k12ac.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
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| File | Version | Author | Date | Message |
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| html | c4358f6 | brimittleman | 2019-06-11 | Build site. |
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| Rmd | 7da06f5 | brimittleman | 2019-05-20 | switch log effect |
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| Rmd | 8f883d8 | brimittleman | 2019-05-20 | add overlap analysis |
This analysis will investigate the sharing between total and nuclear apaQTls first by calculating the pi1 statistic and second by looking at the correlation of effect sizes.
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(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithslibrary(workflowr)This is workflowr version 1.3.0
Run ?workflowr for help getting startedlibrary(qvalue)Pi 1 sharing
Concatinate nominal results and run
mkdir ../data/QTLoverlap/
python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/TotalQTLinNuclearNominal.txt
python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/NuclearQTLinTotalNominal.txt
totAPAinNuc=read.table("../data/QTLoverlap/TotalQTLinNuclearNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_tot=pi0est(totAPAinNuc$pval, pi0.method = "bootstrap")
nucAPAinTot=read.table("../data/QTLoverlap/NuclearQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_nuc=pi0est(nucAPAinTot$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$pval, xlab="Total Pvalue", main="Significant Nuclear APA QTLs \n Total")
text(.8,450, paste("pi_1=", round((1-qval_nuc$pi0), digit=3), sep=" "))
Effect size sharing:
I need to get the nominal effect sizes. I can use the script I wrote above but put the same fraction in for the qtl and nom values.
python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/TotalQTLinTotalNominal.txt
python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/NuclearQTLinNuclearNominal.txt totAPAinTot=read.table("../data/QTLoverlap/TotalQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope) %>% dplyr::rename("Originalslope"=slope)
nucAPAinNuc=read.table("../data/QTLoverlap/NuclearQTLinNuclearNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope)%>% dplyr::rename("Originalslope"=slope)Join the data frames:
Total:
TotBoth= totAPAinNuc %>% inner_join(totAPAinTot,by=c("peakID", "snp"))
summary(lm(log10(TotBoth$slope) ~ log10(TotBoth$Originalslope)))Warning in eval(predvars, data, env): NaNs produced
Warning in eval(predvars, data, env): NaNs produced
Call:
lm(formula = log10(TotBoth$slope) ~ log10(TotBoth$Originalslope))
Residuals:
Min 1Q Median 3Q Max
-2.24040 -0.08718 0.08008 0.15420 0.33426
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.14383 0.01953 -7.363 4.06e-12 ***
log10(TotBoth$Originalslope) 0.81265 0.11379 7.142 1.50e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2836 on 209 degrees of freedom
(195 observations deleted due to missingness)
Multiple R-squared: 0.1962, Adjusted R-squared: 0.1923
F-statistic: 51.01 on 1 and 209 DF, p-value: 1.501e-11ggplot(TotBoth, aes(x=log10(Originalslope), y=log10(slope)))+geom_point() + geom_smooth(method="lm") + labs(title="Total apaQTL effect sizes", x="Effect size in Nuclear",y="Effect size in Total") + geom_density_2d(col="red")Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs producedWarning: Removed 195 rows containing non-finite values (stat_smooth).Warning: Removed 195 rows containing non-finite values (stat_density2d).Warning: Removed 195 rows containing missing values (geom_point).
NucBoth= nucAPAinTot %>% inner_join(nucAPAinNuc,by=c("peakID", "snp"))
summary(lm(log10(NucBoth$slope) ~ log10(NucBoth$Originalslope)))Warning in eval(predvars, data, env): NaNs produced
Warning in eval(predvars, data, env): NaNs produced
Call:
lm(formula = log10(NucBoth$slope) ~ log10(NucBoth$Originalslope))
Residuals:
Min 1Q Median 3Q Max
-2.23491 -0.09433 0.07872 0.18474 0.45126
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.19842 0.01881 -10.546 < 2e-16 ***
log10(NucBoth$Originalslope) 0.96077 0.12546 7.658 3.17e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3141 on 277 degrees of freedom
(310 observations deleted due to missingness)
Multiple R-squared: 0.1747, Adjusted R-squared: 0.1717
F-statistic: 58.64 on 1 and 277 DF, p-value: 3.173e-13ggplot(NucBoth, aes(x=log10(Originalslope), y=log10(slope)))+geom_point() + geom_smooth(method="lm") + labs(title="Nuclear apaQTL effect sizes", x="Effect size in Total",y="Effect size in Nuclear") + geom_density_2d(col="red")Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs producedWarning: Removed 310 rows containing non-finite values (stat_smooth).Warning: Removed 310 rows containing non-finite values (stat_density2d).Warning: Removed 310 rows containing missing values (geom_point).
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 workflowr_1.3.0 reshape2_1.4.3 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[9] tidyr_0.8.3 tibble_2.1.1 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 MASS_7.3-51.1 splines_3.5.1 backports_1.1.2
[41] scales_1.0.0 htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0
[45] colorspace_1.3-2 labeling_0.3 stringi_1.2.4 lazyeval_0.2.1
[49] munsell_0.5.0 broom_0.5.1 crayon_1.3.4