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
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
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    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Deleted:    reads_graphs.Rmd

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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)

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
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
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