Last updated: 2019-09-06
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Knit directory: apaQTL/analysis/
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Rmd | 709b4c8 | brimittleman | 2019-06-30 | add two reg |
library(workflowr)
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In looking at the example plots for the apaQTLs I noticed two types of QTLs. Either a switch QTL or a buffering QTL. I want to see if this is a global classifier. In a switch QTL I expect high variance after i remove the highest effect size PAS and in a buffering QTL I expect low varriance in the effect sizesa after removing the top variant. I will use the normalized nominal effect sizes for this analysis. I will select each PAS for each of the apaQTLs. This means I make a list of the PAS, SNP, gene and select the every line matching one of the snp gene pairs. After this I can group by gene.
mkdir ../data/twoMech
totQTL=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt", header = T, stringsAsFactors = F) %>% select(Peak, Gene, sid)
colnames(totQTL)=c("pas", "gene", "snp")
write.table(totQTL, file="../data/twoMech/TotalapaQTL_PASgeneSNP.txt", col.names = F, row.names = F, quote = F)
nucQTL=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", stringsAsFactors = F, header = T) %>% select(Peak, Gene, sid)
colnames(nucQTL)=c("pas", "gene", "snp")
write.table(nucQTL, file="../data/twoMech/NuclearapaQTL_PASgeneSNP.txt", col.names = F, row.names = F, quote = F)
I will use a python script to pull out the lines I want from the nominal files. It will take a fraction, the input file i created above and an output file.
python pullTwoMechData.py Total ../data/twoMech/TotalapaQTL_PASgeneSNP.txt ../data/twoMech/TotalapaQTL_AllPAS4QTLs.txt
python pullTwoMechData.py Nuclear ../data/twoMech/NuclearapaQTL_PASgeneSNP.txt ../data/twoMech/NuclearapaQTL_AllPAS4QTLs.txt
When I get the results I can remove the lines for the QTLs pas then get the variance. I also need to remove genes with only 2 PAS.
totRes=read.table("../data/twoMech/TotalapaQTL_AllPAS4QTLs.txt", header = T, stringsAsFactors = F)
totGenesInclude=totRes %>% group_by(gene,snp) %>% summarise(nPAS=n()) %>% filter(nPAS>=3)
totRes_filt=totRes %>% filter(gene %in% totGenesInclude$gene) %>% anti_join(totQTL, by=c("snp", "gene", "pas")) %>% group_by(gene, snp) %>% summarize(EffectVar=var(EffectSize)) %>% mutate(fraction="Total")
totRes_filt=na.omit(totRes_filt)
Plot the distribution:
ggplot(totRes_filt,aes(x=EffectVar))+ geom_histogram(bins=50)
Version | Author | Date |
---|---|---|
5edd9c7 | brimittleman | 2019-06-30 |
totRes_filt %>% filter(EffectVar>=1) %>% nrow()
[1] 15
summary(totRes_filt$EffectVar)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000439 0.036342 0.091732 0.301474 0.268952 5.091518
nucRes=read.table("../data/twoMech/NuclearapaQTL_AllPAS4QTLs.txt", header = T, stringsAsFactors = F)
nucGenesInclude=nucRes %>% group_by(gene,snp) %>% summarise(nPAS=n()) %>% filter(nPAS>=3)
nucRes_filt=nucRes %>% filter(gene %in% nucGenesInclude$gene) %>% anti_join(totQTL, by=c("snp", "gene", "pas")) %>% group_by(gene, snp) %>% summarize(EffectVar=var(EffectSize)) %>% mutate(fraction="Nuclear")
nucRes_filt=na.omit(nucRes_filt)
Plot the distribution:
ggplot(nucRes_filt,aes(x=EffectVar))+ geom_histogram(bins=50)
Version | Author | Date |
---|---|---|
5edd9c7 | brimittleman | 2019-06-30 |
nucRes_filt %>% filter(EffectVar>=1) %>% nrow()
[1] 59
summary(nucRes_filt$EffectVar)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000207 0.233162 0.388894 0.564689 0.710507 3.809596
Look at examples:
nucRes_filt %>% arrange(EffectVar) %>% head()
# A tibble: 6 x 4
# Groups: gene [6]
gene snp EffectVar fraction
<chr> <chr> <dbl> <chr>
1 VDR rs58789572 0.000207 Nuclear
2 SCIMP rs63394861 0.000702 Nuclear
3 MAPK8 rs11101320 0.000999 Nuclear
4 UGT2B17 rs143144370 0.00840 Nuclear
5 C22orf34 rs739248 0.00919 Nuclear
6 ZNRD1 rs114653275 0.0128 Nuclear
Difference between fractions?
allEffect=bind_rows(nucRes_filt, totRes_filt)
wilcox.test(nucRes_filt$EffectVar, totRes_filt$EffectVar)
Wilcoxon rank sum test with continuity correction
data: nucRes_filt$EffectVar and totRes_filt$EffectVar
W = 84226, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
ggplot(allEffect, aes(x=fraction, y=EffectVar,fill=fraction)) + geom_boxplot() + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + labs(title="Effect Size Variance in PAS outside of QTL PAS", y="variance(Effect Size)" ) + geom_text(x=1.5, y=5,label="P-value < 2.2*10^-16\n ***")
Version | Author | Date |
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5edd9c7 | brimittleman | 2019-06-30 |
Does this suggest a reduction of variation. More buffering in the total fraction.
I wonder how many overlap?
overlap = inner_join(nucRes_filt, totRes_filt, by=c("gene", "snp"))
colnames(overlap)=c("gene", "snp", "Nuclear_Effect", "Nuclear", "Total_Effect", "Total")
ggplot(overlap, aes(x=log10(Total_Effect), y=log10(Nuclear_Effect))) + geom_point()
Version | Author | Date |
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5edd9c7 | brimittleman | 2019-06-30 |
summary(lm(data=overlap,Nuclear_Effect~Total_Effect))
Call:
lm(formula = Nuclear_Effect ~ Total_Effect, data = overlap)
Residuals:
Min 1Q Median 3Q Max
-0.58394 -0.10028 -0.00921 0.06366 1.05294
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03046 0.04190 0.727 0.47
Total_Effect 0.83277 0.04927 16.901 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2734 on 52 degrees of freedom
Multiple R-squared: 0.846, Adjusted R-squared: 0.843
F-statistic: 285.6 on 1 and 52 DF, p-value: < 2.2e-16
nuclear= .85(total)+.02
This means as nuclear goes up 1 total only goes up .85. Suggest reduction in variation.
I want to add these variance in effect size measurements to the original qtls.
totQTL_vareff=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt", header = T, , col.names = c('Chr', 'Start', 'End', 'gene', 'Loc', 'Strand', 'peak', 'nvar', 'shape1', 'shape2', 'dummy', 'snp', 'dist', 'npval', 'slope', 'ppval', 'bpval', 'bh'),stringsAsFactors = F) %>% inner_join(totRes_filt, by=c("gene", "snp")) %>% filter(Loc %in% c("utr3", "intron"))
totQTL_vareff_int= totQTL_vareff %>% filter(Loc=="intron")
totQTL_vareff_utr= totQTL_vareff %>% filter(Loc=="utr3")
t.test(totQTL_vareff_int$EffectVar, totQTL_vareff_utr$EffectVar , alternative = "less")
Welch Two Sample t-test
data: totQTL_vareff_int$EffectVar and totQTL_vareff_utr$EffectVar
t = -0.21437, df = 115.52, p-value = 0.4153
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
-Inf 0.1561782
sample estimates:
mean of x mean of y
0.313021 0.336210
ggplot(totQTL_vareff,aes(x=Loc, y=log10(EffectVar), fill=Loc) )+ geom_boxplot() + scale_fill_manual(values=c("blue","orange")) + labs(title="No difference in QTL type between total PAS location", x="PAS Location") + geom_text(x=1.5,y=0.5, label="P-value= 0.42")
Version | Author | Date |
---|---|---|
5edd9c7 | brimittleman | 2019-06-30 |
nucQTL_vareff=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", header = T, , col.names = c('Chr', 'Start', 'End', 'gene', 'Loc', 'Strand', 'peak', 'nvar', 'shape1', 'shape2', 'dummy', 'snp', 'dist', 'npval', 'slope', 'ppval', 'bpval', 'bh'),stringsAsFactors = F) %>% inner_join(totRes_filt, by=c("gene", "snp")) %>% filter(Loc %in% c("utr3", "intron"))
nucQTL_vareff_int= nucQTL_vareff %>% filter(Loc=="intron")
nucQTL_vareff_utr= nucQTL_vareff %>% filter(Loc=="utr3")
t.test(nucQTL_vareff_int$EffectVar, nucQTL_vareff_utr$EffectVar , alternative = "less")
Welch Two Sample t-test
data: nucQTL_vareff_int$EffectVar and nucQTL_vareff_utr$EffectVar
t = -2.3839, df = 49.059, p-value = 0.01052
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
-Inf -0.1153955
sample estimates:
mean of x mean of y
0.2191955 0.6080749
ggplot(nucQTL_vareff,aes(x=Loc, y=log10(EffectVar), fill=Loc) )+ geom_boxplot() + scale_fill_manual(values=c("blue","orange")) + labs(title="Nuclear Intronic QTL more likely to lead to buffering", x="PAS Location") + geom_text(x=1.5,y=0.5, label="P-value= 0.01\n***")
Version | Author | Date |
---|---|---|
5edd9c7 | brimittleman | 2019-06-30 |
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1 workflowr_1.4.0
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 fansi_0.4.0 readxl_1.1.0 rmarkdown_1.10
[37] modelr_0.1.2 magrittr_1.5 whisker_0.3-2 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 utf8_1.1.4 stringi_1.2.4
[49] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1 crayon_1.3.4