Last updated: 2018-10-11

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Unstaged changes:
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    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
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    Modified:   analysis/overlap_qtls.Rmd
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    File Version Author Date Message
    Rmd f819c8e Briana Mittleman 2018-10-11 add distance metric analsis total apaQTL

This analysis will be used to characterize the total ApaQTLs. I will run the analysis on the total APAqtls in this analysis and will then run a similar analysis on the nuclear APAqtls in another analysis. I would like to study:

  • Distance metrics:
    • distance from snp to TSS of gene
    • Distance from snp to peak
  • Expression metrics:
    • expression of genes with significant QTLs vs other genes (by rna seq)
    • expression of genes with significant QTLs vs other genes (peak coverage)
  • Chrom HMM metrics:
    • look at the chrom HMM interval for the significant QTLs

Upload Libraries and Data:

Library

library(workflowr)
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library(reshape2)
library(tidyverse)
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library(VennDiagram)
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library(data.table)

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library(cowplot)

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Permuted Results from APA:

I will add a column to this dataframe that will tell me if the association is significant at 10% FDR. This will help me plot based on significance later in the analysis. I am also going to seperate the PID into relevant pieces.

totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T)  %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>%  separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak"))

totalAPA$sig=as.factor(totalAPA$sig)


print(names(totalAPA))
 [1] "chr"    "start"  "end"    "gene"   "strand" "peak"   "nvar"  
 [8] "shape1" "shape2" "dummy"  "sid"    "dist"   "npval"  "slope" 
[15] "ppval"  "bpval"  "bh"     "sig"

Distance Metrics

Distance from snp to TSS

I ran the QTL analysis based on the starting position of the gene.

ggplot(totalAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp to TSS", x="Base Pairs") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))

It looks like most of the signifcant values are 100,000 bases. This makes sense. I can zoom in on this portion.

ggplot(totalAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)+coord_cartesian(xlim = c(-150000, 150000))

Distance from snp to peak

To perform this analysis I need to recover the peak positions.

The peak file I used for the QTL analysis is: /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed

peaks=read.table("../data/PeaksUsed/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed", col.names = c("chr", "peakStart", "peakEnd", "PeakNum", "PeakScore", "Strand", "Gene")) %>% mutate(peak=paste("peak", PeakNum,sep="")) %>% mutate(PeakCenter=peakStart+ (peakEnd- peakStart))

I want to join the peak start to the totalAPA file but the peak column. I will then create a column that is snppos-peakcenter.

totalAPA_peakdist= totalAPA %>%  inner_join(peaks, by="peak") %>%  separate(sid, into=c("snpCHR", "snpLOC"), by=":")
totalAPA_peakdist$snpLOC= as.numeric(totalAPA_peakdist$snpLOC)

totalAPA_peakdist= totalAPA_peakdist %>%  mutate(DisttoPeak= snpLOC-PeakCenter)

Plot this by significance.

ggplot(totalAPA_peakdist, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp peak", x="log10 absolute value Distance to Peak") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))

Look at the summarys based on significance:

totalAPA_peakdist_sig=totalAPA_peakdist %>% filter(sig==1)
totalAPA_peakdist_notsig=totalAPA_peakdist %>% filter(sig==0)


summary(totalAPA_peakdist_sig$DisttoPeak)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-634474.0  -26505.0   -3325.5  -23883.8     492.5  460051.0 
summary(totalAPA_peakdist_notsig$DisttoPeak)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-70147526   -269853     -1269     -6620    266370   5433999 
ggplot(totalAPA_peakdist, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))

Look like there are some outliers that are really far. I will remove variants greater than 1*10^6th away

totalAPA_peakdist_filt=totalAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))

ggplot(totalAPA_peakdist_filt, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)

ggplot(totalAPA_peakdist_filt, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)

This gives a similar distribution.

I did snp - peak. This means if the peak is downstream of the snp on the positive strand the number will be negative.

In this case the peak is downstream of the snp.

totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="+") %>%  filter(DisttoPeak < 0) %>% nrow()
[1] 45
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="-") %>%  filter(DisttoPeak > 0) %>% nrow()
[1] 16

Peak is upstream of the snp.

totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="+") %>%  filter(DisttoPeak > 0) %>% nrow()
[1] 17
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="-") %>%  filter(DisttoPeak < 0) %>% nrow()
[1] 40

This means there is about 50/50 distribution around the peak start.

I am going to plot a violin plot for just the significant ones.

ggplot(totalAPA_peakdist_sig, aes(x=DisttoPeak)) + geom_density()

Within 1000 bases of the peak center.

totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()
[1] 29
totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()
[1] 57
totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()
[1] 98

29 QTLs are within 1000 bp of the peak center, 57 within 10,000bp and 98 within 100,000bp

Expression metrics

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] bindrcpp_0.2.2      cowplot_0.9.3       data.table_1.11.8  
 [4] VennDiagram_1.6.20  futile.logger_1.4.3 forcats_0.3.0      
 [7] stringr_1.3.1       dplyr_0.7.6         purrr_0.2.5        
[10] readr_1.1.1         tidyr_0.8.1         tibble_1.4.2       
[13] ggplot2_3.0.0       tidyverse_1.2.1     reshape2_1.4.3     
[16] workflowr_1.1.1    

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4     haven_1.1.2          lattice_0.20-35     
 [4] colorspace_1.3-2     htmltools_0.3.6      yaml_2.2.0          
 [7] rlang_0.2.2          R.oo_1.22.0          pillar_1.3.0        
[10] glue_1.3.0           withr_2.1.2          R.utils_2.7.0       
[13] lambda.r_1.2.3       modelr_0.1.2         readxl_1.1.0        
[16] bindr_0.1.1          plyr_1.8.4           munsell_0.5.0       
[19] gtable_0.2.0         cellranger_1.1.0     rvest_0.3.2         
[22] R.methodsS3_1.7.1    evaluate_0.11        labeling_0.3        
[25] knitr_1.20           broom_0.5.0          Rcpp_0.12.19        
[28] formatR_1.5          backports_1.1.2      scales_1.0.0        
[31] jsonlite_1.5         hms_0.4.2            digest_0.6.17       
[34] stringi_1.2.4        rprojroot_1.3-2      cli_1.0.1           
[37] tools_3.5.1          magrittr_1.5         lazyeval_0.2.1      
[40] futile.options_1.0.1 crayon_1.3.4         whisker_0.3-2       
[43] pkgconfig_2.0.2      xml2_1.2.0           lubridate_1.7.4     
[46] assertthat_0.2.0     rmarkdown_1.10       httr_1.3.1          
[49] rstudioapi_0.8       R6_2.3.0             nlme_3.1-137        
[52] git2r_0.23.0         compiler_3.5.1

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