Last updated: 2019-06-30

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
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    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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Rmd 709b4c8 brimittleman 2019-06-30 add two reg 
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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)

totRes_filt %>% filter(EffectVar>=1) %>% nrow()
[1] 12
summary(totRes_filt$EffectVar)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000203 0.031257 0.076122 0.268597 0.207242 5.380624 
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)

nucRes_filt %>% filter(EffectVar>=1) %>% nrow()
[1] 66
summary(nucRes_filt$EffectVar)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.2068  0.3775  0.5569  0.6737  3.8261

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 ZNF418  rs7253514   0.000000349 Nuclear 
2 ZNRD1   rs114653275 0.00000290  Nuclear 
3 PSMF1   rs1217      0.0000383   Nuclear 
4 SPIB    rs112787272 0.00128     Nuclear 
5 UGT2B17 rs143144370 0.00439     Nuclear 
6 TRAF3   rs72704737  0.00685     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 = 116480, 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 ***")

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()

summary(lm(data=overlap,Nuclear_Effect~Total_Effect))

Call:
lm(formula = Nuclear_Effect ~ Total_Effect, data = overlap)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.32650 -0.08316 -0.01112  0.03631  1.03706 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   0.02480    0.04626   0.536    0.594    
Total_Effect  0.85488    0.04571  18.701   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3173 on 56 degrees of freedom
Multiple R-squared:  0.862, Adjusted R-squared:  0.8595 
F-statistic: 349.7 on 1 and 56 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 = -0.68239, df = 143, p-value = 0.248
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
       -Inf 0.09914162
sample estimates:
mean of x mean of y 
0.2463218 0.3158391 
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=  0.25") 

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 = -2.5833, df = 42.393, p-value = 0.006661
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
      -Inf -0.172268
sample estimates:
mean of x mean of y 
0.1616422 0.6551801 
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= 0.00067\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] 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 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] fansi_0.4.0      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     utf8_1.1.4       stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4