Location of apaQTLs

Last updated: 2019-09-04

Checks: 6 1

Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    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
    Modified:   code/environment.yaml
    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:    docs/figure/QTLlocation.Rmd/figure2B-1.pdf
    Deleted:    docs/figure/QTLlocation.Rmd/figure2C-1.pdf
    Deleted:    reads_graphs.Rmd

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File	Version	Author	Date	Message
Rmd	73f5833	brimittleman	2019-09-04	wflow_publish(c(“analysis/QTLlocation.Rmd”, “analysis/apaQTLoverlap.Rmd”, “analysis/nonNormQTL.Rmd”,
html	d1a2bed	brimittleman	2019-08-01	Build site.
Rmd	1ef5cad	brimittleman	2019-08-01	figure2b2c
html	c5bf3fd	brimittleman	2019-07-02	Build site.
Rmd	36d86c0	brimittleman	2019-07-02	post LM plot midifications
html	62ab1cc	brimittleman	2019-06-21	Build site.
Rmd	f0e8ce1	brimittleman	2019-06-21	merge fracs
html	c0a090f	brimittleman	2019-06-21	Build site.
html	4912eaa	brimittleman	2019-06-14	Build site.
Rmd	bf91cb3	brimittleman	2019-06-14	add location plot

In this analysis I want to look at the location of the apaQTLs first looking at distance to PAS. Until now I have been using the distance to the peak and have not flipped the strand. This showed me QTLs are close to the PAS but was not the most correct way to do this.

library(workflowr)

This is workflowr version 1.4.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

PAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed",col.names = c("chr", "start", "PASloc", "name", "score", "strand"), stringsAsFactors = F )%>% separate(name, into=c("peakNum", "geneloc"), sep=":") %>% mutate(peak=paste("peak", peakNum, sep="")) %>% select(PASloc, peak)

Distance to PAS

totQTL_bed=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.bed", stringsAsFactors = F, header = T) 
totQTL_txt=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt", stringsAsFactors = F, header = T)%>% mutate(name=paste(Gene, Peak, Loc, sep=":")) 
totQTL_strand=totQTL_bed %>% full_join(totQTL_txt, by="name") %>% mutate(strand=ifelse(Strand=="+", "-", "+")) %>% select(SNPchr, SNPstart, SNPend, name, score, strand)

write.table(totQTL_strand, "../data/apaQTLs/Total_apaQTLs4pc_5fdr.WITHSTRAND.bed", quote = F, col.names = T, row.names = F)


nucQTL_bed=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.bed", stringsAsFactors = F, header = T) 
nucQTL_txt=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", stringsAsFactors = F, header = T)%>% mutate(name=paste(Gene, Peak, Loc, sep=":")) 
nucQTL_strand=nucQTL_bed %>% full_join(nucQTL_txt, by="name") %>% mutate(strand=ifelse(Strand=="+", "-", "+")) %>% select(SNPchr, SNPstart, SNPend, name, score, strand)

write.table(nucQTL_strand, "../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.WITHSTRAND.bed", quote = F, col.names = T, row.names = F)

Total:

totQTLs=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.WITHSTRAND.bed",stringsAsFactors = F, header=T)%>%
  separate(name, into=c("gene", "peak", "loc"), sep=":") %>% 
  inner_join(PAS, by="peak") %>% 
  mutate(distance=SNPend-PASloc, dist2PAS=ifelse(strand=="-", -1 *distance, distance))

ggplot(totQTLs, aes(x=dist2PAS, by=loc, fill=loc)) + geom_histogram(bins=100)

Version	Author	Date
4912eaa	brimittleman	2019-06-14

Plot by proportion:

ggplot(totQTLs, aes(x=dist2PAS, fill=loc)) + geom_histogram( bins=100) + facet_grid(~loc)

Nuclear

nucQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.WITHSTRAND.bed",stringsAsFactors = F, header=T)%>%
  separate(name, into=c("gene", "peak", "loc"), sep=":") %>% 
  inner_join(PAS, by="peak") %>% 
  mutate(distance=SNPend-PASloc, dist2PAS=ifelse(strand=="-", -1 *distance, distance))

ggplot(nucQTLs, aes(x=dist2PAS, by=loc, fill=loc)) + geom_histogram(bins=100)

Version	Author	Date
4912eaa	brimittleman	2019-06-14

ggplot(nucQTLs, aes(x=dist2PAS, fill=loc)) + geom_histogram( bins=100) + facet_grid(~loc)

Version	Author	Date
62ab1cc	brimittleman	2019-06-21

Plot total and nuclear together:

totalQTLdist=totQTLs %>% select(dist2PAS) %>% mutate(Fraction="Total")
nuclearQTLdist=nucQTLs %>% select(dist2PAS) %>% mutate(Fraction="Nuclear")
bothFractDist=bind_rows(totalQTLdist, nuclearQTLdist)

PASdistplot=ggplot(bothFractDist, aes(x=dist2PAS, fill=Fraction )) + geom_histogram(bins=100,aes(y = (..count..)/sum(..count..))) +labs(y="Proportion of apaQTLs", x="Distance QTL SNP to PAS", title="Distance from QTL SNP to PAS by Fraction") + scale_fill_manual(values=c("deepskyblue3","darkviolet"))  + facet_grid(~Fraction) + theme(text = element_text(size=16),legend.position = "bottom", axis.text.x =element_text(size=14), axis.text.y=element_text(size=14))

ggplot(bothFractDist, aes(x=dist2PAS, fill=Fraction )) + geom_density() +labs( x="Distance QTL SNP to PAS", title="Distance from QTL SNP to PAS") + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + facet_grid(~Fraction) + theme(legend.position = "none")

Version	Author	Date
4912eaa	brimittleman	2019-06-14

Metagene plot

I want to plot by normalized position in the gene.

genes=read.table("../../genome_anotation_data/refseq.ProteinCoding.bed",col.names = c("chrom", "Genestart", "Geneend", "gene", "score", "strand") ,stringsAsFactors = F) %>% select(Genestart, Geneend, gene)

Total:

totQTLs_gene= totQTLs %>% inner_join(genes, by="gene")%>% mutate(geneLength=Geneend-Genestart) %>% mutate(dist2QTL=ifelse(strand=="+",as.numeric(SNPend)-as.numeric(Genestart), as.numeric(Geneend)-as.numeric(SNPend)), propGene=dist2QTL/geneLength)  %>% filter(propGene>-3 & propGene<3)

ggplot(totQTLs_gene, aes(x=propGene, fill=loc)) + geom_histogram(bins=50)  + labs(x="Proportion of gene body", y="number QTLs", title="Total apaQTLs") + geom_vline(xintercept =0,color= "black") + geom_vline(xintercept =1,color= "black")

Version	Author	Date
d1a2bed	brimittleman	2019-08-01
c5bf3fd	brimittleman	2019-07-02
4912eaa	brimittleman	2019-06-14

totQTLs_gene_intron_utr= totQTLs_gene %>% filter(loc=="intron"| loc =="utr3")
ggplot(totQTLs_gene_intron_utr, aes(x=propGene, fill=loc)) + geom_density()  + labs(x="Proportion of gene body", title="Total apaQTLs") + geom_vline(xintercept =0,color= "black") + geom_vline(xintercept =1,color= "black")

Version	Author	Date
d1a2bed	brimittleman	2019-08-01
62ab1cc	brimittleman	2019-06-21
4912eaa	brimittleman	2019-06-14

There are about 48 QTLs outside.

I can look at only those in the gene body:

totQTLs_gene_body= totQTLs_gene %>% filter(propGene>=0, propGene<=1)
  
ggplot(totQTLs_gene_body, aes(x=propGene, fill=loc)) + geom_histogram(bins=50)  + labs(x="Proportion of gene body", y="number QTLs", title="Total apaQTLs in gene body")

Version	Author	Date
c5bf3fd	brimittleman	2019-07-02
62ab1cc	brimittleman	2019-06-21

There are 181 in the gene body

Nuclear:

nucQTLs_gene= nucQTLs %>% inner_join(genes, by="gene")%>% mutate(geneLength=Geneend-Genestart)%>% mutate(dist2QTL=ifelse(strand=="+",as.numeric(SNPend)-as.numeric(Genestart), as.numeric(Geneend)-as.numeric(SNPend)), propGene=dist2QTL/geneLength)  %>% filter(propGene>-3 & propGene<3)

ggplot(nucQTLs_gene, aes(x=propGene, fill=loc)) + geom_histogram(bins=50)  + labs(x="Proportion of gene body", y="number QTLs", title="Nuclear apaQTLs") + geom_vline(xintercept =0,color= "black") + geom_vline(xintercept =1,color= "black")

Version	Author	Date
d1a2bed	brimittleman	2019-08-01
c5bf3fd	brimittleman	2019-07-02
62ab1cc	brimittleman	2019-06-21

nucQTLs_gene_intron_utr= nucQTLs_gene %>% filter(loc=="intron"| loc =="utr3")
ggplot(nucQTLs_gene_intron_utr, aes(x=propGene, fill=loc)) + geom_density()  + labs(x="Proportion of gene body", title="Nuclear apaQTLs") + geom_vline(xintercept =0,color= "black") + geom_vline(xintercept =1,color= "black")

Version	Author	Date
d1a2bed	brimittleman	2019-08-01
c5bf3fd	brimittleman	2019-07-02

there are 77 outside of 500% of gene body

nucQTLs_gene_body= nucQTLs_gene %>% filter(propGene>=0, propGene<=1)

ggplot(nucQTLs_gene_body, aes(x=propGene, fill=loc)) + geom_histogram(bins=50)  + labs(x="Proportion of gene body", y="number QTLs", title="Nuclear apaQTLs in gene body")

Version	Author	Date
d1a2bed	brimittleman	2019-08-01
c5bf3fd	brimittleman	2019-07-02

334 are in the gene body.

Plot both togther:

nucQTLs_geneprop= nucQTLs_gene %>% select(propGene) %>% mutate(Fraction="Nuclear")
totQTLs_geneprop= totQTLs_gene %>% select(propGene) %>% mutate(Fraction="Total")
genepropboth=bind_rows(totQTLs_geneprop,nucQTLs_geneprop)

ggplot(genepropboth, aes(x=propGene,fill=Fraction)) + geom_histogram(bins=100) + geom_vline(xintercept =0,color= "black") + geom_vline(xintercept =1,color= "black")+ scale_fill_manual(values=c("deepskyblue3","darkviolet"))  + labs(x="Proportion of gene body", y="Number of apaQTLs", title="Metagene plot for apaQTL SNP location")

Version	Author	Date
d1a2bed	brimittleman	2019-08-01

Density plot

propgenebodyplot=ggplot(genepropboth, aes(x=propGene,fill=Fraction)) + geom_histogram(bins=100,aes(y = (..count..)/sum(..count..))) + geom_vline(xintercept =0,color= "black") + geom_vline(xintercept =1,color= "black")+ scale_fill_manual(values=c("deepskyblue3","darkviolet"))  + labs(y="Proportion of qtls",x="Proportion of gene body", title="Metagene plot for apaQTL SNP location")+  theme(text = element_text(size=16),legend.position = "bottom", axis.text.x =element_text(size=14), axis.text.y=element_text(size=14)) + facet_grid(~Fraction)
propgenebodyplot

Version	Author	Date
d1a2bed	brimittleman	2019-08-01

geneprop_nuc=genepropboth %>% filter(Fraction=="Nuclear")

ggplot(geneprop_nuc, aes(x=propGene)) + geom_density(bins=100, fill="#7570B3") + geom_vline(xintercept =0,color= "black") + geom_vline(xintercept =1,color= "black")  + labs(x="Proportion of gene body", title="apaQTL SNP location")+  theme(text = element_text(size=16), axis.text.x =element_text(size=14), axis.text.y=element_text(size=14))

Warning: Ignoring unknown parameters: bins

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] cowplot_0.9.4   forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   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         readxl_1.1.0     rmarkdown_1.10   reshape2_1.4.3  
[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     stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Location of apaQTLs

Briana Mittleman

6/14/2019

Distance to PAS

Metagene plot