Last updated: 2019-09-04
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Knit directory: apaQTL/analysis/
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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':
smiths
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
library(qvalue)
Concatinate nominal results and run
#concatinate nominal res
cat ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr* >../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt
cat ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_chr* >../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt
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$pval, xlab="Total Pvalue", main="Significant Nuclear APA QTLs \n Total")
text(.8,350, paste("pi_1=", round((1-qval_nuc$pi0), digit=3), sep=" "))
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
-1.98510 -0.09337 0.09067 0.16879 0.35206
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.17332 0.02196 -7.892 2.26e-13 ***
log10(TotBoth$Originalslope) 0.98512 0.13015 7.569 1.56e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3041 on 191 degrees of freedom
(210 observations deleted due to missingness)
Multiple R-squared: 0.2307, Adjusted R-squared: 0.2267
F-statistic: 57.29 on 1 and 191 DF, p-value: 1.556e-12
ggplot(TotBoth, aes(x=log10(Originalslope), y=log10(slope)))+geom_point() + geom_smooth(method="lm") + labs(title="Total apaQTL effect sizes", x="Effect size in Nuclear",y="Effect size in Total") + geom_density_2d(col="red")
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning in FUN(X[[i]], ...): NaNs produced
Warning: Removed 210 rows containing non-finite values (stat_smooth).
Warning: Removed 210 rows containing non-finite values (stat_density2d).
Warning: Removed 210 rows containing missing values (geom_point).
NucBoth= nucAPAinTot %>% inner_join(nucAPAinNuc,by=c("peakID", "snp"))
summary(lm(log10(NucBoth$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$Originalslope))
Residuals:
Min 1Q Median 3Q Max
-2.36694 -0.09634 0.07094 0.18822 0.43829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.17159 0.02105 -8.150 2.65e-14 ***
log10(NucBoth$Originalslope) 0.78145 0.13940 5.606 6.11e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3142 on 222 degrees of freedom
(259 observations deleted due to missingness)
Multiple R-squared: 0.124, Adjusted R-squared: 0.1201
F-statistic: 31.43 on 1 and 222 DF, p-value: 6.113e-08
ggplot(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 produced
Warning: Removed 259 rows containing non-finite values (stat_smooth).
Warning: Removed 259 rows containing non-finite values (stat_density2d).
Warning: Removed 259 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_1.4.0 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] tidyselect_0.2.5 splines_3.5.1 haven_1.1.2 lattice_0.20-38
[5] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6 yaml_2.2.0
[9] rlang_0.4.0 pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] modelr_0.1.2 readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[17] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2 evaluate_0.12
[21] labeling_0.3 knitr_1.20 highr_0.7 broom_0.5.1
[25] Rcpp_1.0.0 scales_1.0.0 backports_1.1.2 jsonlite_1.6
[29] fs_1.3.1 hms_0.4.2 digest_0.6.18 stringi_1.2.4
[33] grid_3.5.1 rprojroot_1.3-2 cli_1.1.0 tools_3.5.1
[37] magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[41] pkgconfig_2.0.2 MASS_7.3-51.1 xml2_1.2.0 lubridate_1.7.4
[45] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10
[49] R6_2.3.0 nlme_3.1-137 git2r_0.25.2 compiler_3.5.1