Characterize Nuclear ApaQTLs

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.

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")

### 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.

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.

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.

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.

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

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

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=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 ="[.]")

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)

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 

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.

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.

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")

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))

I do still need to get 880 random snps.

shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random880.txt

Run QTLNOMres2SigSNPbed.py with nuclear 880 and sort output

import pybedtools 

RANDnuc=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random880.sort.bed') 



hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")

#map hmm to snps  
NucRnad_overlapHMM=RANDnuc.map(hmm, c=4)


#save results  

NucRnad_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random_overlapHMM.bed")

NuclearRandOverlapHMM=read.table("../data/ChromHmmOverlap/ApaQTL_nuclear_Random_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))

NuclearRandOverlapHMM_names=NuclearRandOverlapHMM %>% left_join(chromHmm, by="number")

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

To put this on the same plot I can count the number in each then plot them next to eachother.

random_perChromHMM_nuc=NuclearRandOverlapHMM_names %>%  group_by(name) %>% summarise(Random=n())
sig_perChromHMM_nuc= NuclearOverlapHMM_names %>%  group_by(name) %>%  summarise(Nuclear_QTLs=n())

perChrommHMM_nuc=random_perChromHMM_nuc %>%  full_join(sig_perChromHMM_nuc, by="name", ) %>% replace_na(list(Random=0,Total_QTLs=0))  

perChrommHMM_nuc_melt=melt(perChrommHMM_nuc, id.vars="name")
names(perChrommHMM_nuc_melt)=c("Region","Set", "N_Snps" )

chromenrichNuclearplot=ggplot(perChrommHMM_nuc_melt, aes(x=Region, y=N_Snps, by=Set, fill=Set)) + geom_bar(position="dodge", stat="identity") +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Enrichment of Nuclear QTLs by chromatin region", y="Number of Snps", x="Chromatin Region") + scale_fill_brewer(palette="Paired")
chromenrichNuclearplot

ggsave("../output/plots/ChromHmmEnrich_Nuclear.png", chromenrichNuclearplot)

Saving 7 x 5 in image