Characterize Nuclear ApaQTLs

Last updated: 2019-02-15

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Knit directory: threeprimeseq/analysis/

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File	Version	Author	Date	Message
html	de24d7d	Briana Mittleman	2019-01-15	Build site.
Rmd	57fbf88	Briana Mittleman	2019-01-15	update dist to peak plots
html	2ec5ffd	Briana Mittleman	2018-11-07	Build site.
Rmd	962e39b	Briana Mittleman	2018-11-07	move chromhmm analysis to its own analysis
html	a7ecc84	Briana Mittleman	2018-11-07	Build site.
Rmd	e43bd07	Briana Mittleman	2018-11-07	group chromhmm by number
html	b176cda	Briana Mittleman	2018-11-06	Build site.
Rmd	75467a1	Briana Mittleman	2018-11-06	load in permutation results
html	a5b4cf6	Briana Mittleman	2018-10-29	Build site.
Rmd	afb0ce9	Briana Mittleman	2018-10-29	change plot colors
html	805dec6	Briana Mittleman	2018-10-26	Build site.
Rmd	5cb6b0b	Briana Mittleman	2018-10-26	permutation code
html	96cfdcd	Briana Mittleman	2018-10-24	Build site.
Rmd	00b1020	Briana Mittleman	2018-10-24	naive enrichment
html	de860f0	Briana Mittleman	2018-10-24	Build site.
Rmd	96a97f4	Briana Mittleman	2018-10-24	add nuclear characterization

This analysis is similar to the Characterize Total APAqtl 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)

This is workflowr version 1.2.0
Run ?workflowr for help getting started

library(reshape2)
library(tidyverse)

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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.

NuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_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"))

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


print(names(NuclearAPA))

 [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(NuclearAPA, 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")) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

Zoom in to 100,000.

ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)+coord_cartesian(xlim = c(-150000, 150000)) + scale_fill_brewer(palette="Paired")

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

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 NuclearAPA file but the peak column. I will then create a column that is snppos-peakcenter.

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

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

Plot this by significance.

ggplot(NuclearAPA_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")) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

Look at the summarys based on significance:

NuclearAPA_peakdist_sig=NuclearAPA_peakdist %>% filter(sig==1)
NuclearAPA_peakdist_notsig=NuclearAPA_peakdist %>% filter(sig==0)


summary(NuclearAPA_peakdist_sig$DisttoPeak)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-1003786   -17579      -91    -8818     6588   891734

summary(NuclearAPA_peakdist_notsig$DisttoPeak)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-70147526   -265059     -2067      7263    255169 125172864

ggplot(NuclearAPA_peakdist, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

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

NuclearAPA_peakdist_filt=NuclearAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))

ggplot(NuclearAPA_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) + scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

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

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

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

ggplot(NuclearAPA_peakdist_sig, aes(x=log10(abs(DisttoPeak)+1))) + geom_density(fill="deepskyblue3")+ labs(title="Nuclear: Distance from QTL to PAS Peak", x="Distance from SNP to PAS")

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
a5b4cf6	Briana Mittleman	2018-10-29
de860f0	Briana Mittleman	2018-10-24

Within 1000 bases of the peak center.

NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()

[1] 192

NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()

[1] 420

NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()

[1] 726

192 QTLs are within 1000 bp, 420 are within 10000, and 726 are within 100,000bp

Expression metrics

Next I want to pull in the expression values and compare the expression of genes with a nuclear APA qtl in comparison to genes without one. I will also need to pull in the gene names file to add in the gene names from the ensg ID.

Remove the # from the file.

expression=read.table("../data/mol_pheno/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt", header = T,stringsAsFactors = F)
expression_mean=apply(expression[,5:73],1,mean,na.rm=TRUE)
expression_var=apply(expression[,5:73],1,var,na.rm=TRUE)
expression$exp.mean= expression_mean 
expression$exp.var=expression_var
expression= expression %>% separate(ID, into=c("Gene.stable.ID", "ver"), sep ="[.]")

Now I can pull in the names and join the dataframes.

geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F) 

expression=expression %>% inner_join(geneNames,by="Gene.stable.ID") 

expression=expression %>% select(Chr, start, end, Gene.name, exp.mean,exp.var) %>%  rename("gene"=Gene.name)

Now I can join this with the qtls.

NuclearAPA_wExp=NuclearAPA %>% inner_join(expression, by="gene")

gene_wQTL_N= NuclearAPA_wExp %>% group_by(gene) %>% summarise(sig_gene=sum(as.numeric(as.character(sig)))) %>% ungroup() %>% inner_join(expression, by="gene") %>% mutate(sigGeneFactor=ifelse(sig_gene>=1, 1,0))

gene_wQTL_N$sigGeneFactor= as.factor(as.character(gene_wQTL_N$sigGeneFactor))
summary(gene_wQTL_N$sigGeneFactor)

   0    1 
4589  607

There are 607 genes with a QTL

ggplot(gene_wQTL_N, aes(x=exp.mean, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) +labs(title="Mean in RNA expression by genes with significant QTL", x="Mean in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

I can do a similar analysis but test the variance in the gene expression.

ggplot(gene_wQTL_N, aes(x=exp.var, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) + labs(title="Varriance in RNA expression by genes with significant QTL", x="Variance in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")

Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

Peak coverage for QTLs

I can also look at peak coverage for peaks with QLTs and those without. I will first look at this on peak level then mvoe to gene level. The peak scores come from the coverage in the peaks.

The NuclearAPA_peakdist data frame has the information I need for this.

ggplot(NuclearAPA_peakdist, aes(x=PeakScore,fill=sig,by=sig)) + geom_density(alpha=.5)+ scale_x_log10() + labs(title="Peak score by significance") + scale_fill_brewer(palette="Paired")

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

This is expected. It makes sense that we have more power to detect qtls in higher expressed peaks. This leads me to believe that filtering out low peaks may add power but will not mitigate the effect. ##Where are the SNPs

I created the significant SNP files in the Characterize Total APAqtl analysis analysis.

chromHmm=read.table("../data/ChromHmmOverlap/chromHMM_regions.txt", col.names = c("number", "name"), stringsAsFactors = F)

NuclearOverlapHMM=read.table("../data/ChromHmmOverlap/Nuc_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))
NuclearOverlapHMM$number=as.integer(NuclearOverlapHMM$number)
NuclearOverlapHMM_names=NuclearOverlapHMM %>% left_join(chromHmm, by="number")

ggplot(NuclearOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Nuclear APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))

Version	Author	Date
de24d7d	Briana Mittleman	2019-01-15
de860f0	Briana Mittleman	2018-10-24

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

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       ggpubr_0.1.8       
 [4] magrittr_1.5        data.table_1.11.8   VennDiagram_1.6.20 
 [7] futile.logger_1.4.3 forcats_0.3.0       stringr_1.4.0      
[10] dplyr_0.7.6         purrr_0.2.5         readr_1.1.1        
[13] tidyr_0.8.1         tibble_1.4.2        ggplot2_3.0.0      
[16] tidyverse_1.2.1     reshape2_1.4.3      workflowr_1.2.0    

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          pillar_1.3.0         glue_1.3.0          
[10] withr_2.1.2          RColorBrewer_1.1-2   modelr_0.1.2        
[13] lambda.r_1.2.3       readxl_1.1.0         bindr_0.1.1         
[16] plyr_1.8.4           munsell_0.5.0        gtable_0.2.0        
[19] cellranger_1.1.0     rvest_0.3.2          evaluate_0.13       
[22] labeling_0.3         knitr_1.20           broom_0.5.0         
[25] Rcpp_0.12.19         formatR_1.5          scales_1.0.0        
[28] backports_1.1.2      jsonlite_1.6         fs_1.2.6            
[31] hms_0.4.2            digest_0.6.17        stringi_1.2.4       
[34] rprojroot_1.3-2      cli_1.0.1            tools_3.5.1         
[37] lazyeval_0.2.1       futile.options_1.0.1 crayon_1.3.4        
[40] whisker_0.3-2        pkgconfig_2.0.2      xml2_1.2.0          
[43] lubridate_1.7.4      assertthat_0.2.0     rmarkdown_1.11      
[46] httr_1.3.1           rstudioapi_0.9.0     R6_2.3.0            
[49] nlme_3.1-137         git2r_0.24.0         compiler_3.5.1