Location of apaQTLs

Last updated: 2019-06-21

Checks: 6 0

Knit directory: apaQTL/analysis/

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    Modified:   analysis/NuclearSpecAPAqtl.Rmd
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    Modified:   code/clusterfiltPAS.json
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    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
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 1.3.0
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

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)

Version	Author	Date
4912eaa	brimittleman	2019-06-14

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
4912eaa	brimittleman	2019-06-14

Metagene plot

I want to plot by normalized position in the gene.

genes=tss=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(dist2QTLnostrand= as.numeric(SNPend)-as.numeric(Genestart), dist2QTL=ifelse(strand=="-", -1 *dist2QTLnostrand, dist2QTLnostrand), propGene=dist2QTL/geneLength)  %>% filter(propGene>-5 & propGene<5)

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
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
4912eaa	brimittleman	2019-06-14

There are 181 in the gene body

Nuclear:

nucQTLs_gene= nucQTLs %>% inner_join(genes, by="gene")%>% mutate(geneLength=Geneend-Genestart) %>% mutate(dist2QTLnostrand= as.numeric(SNPend)-as.numeric(Genestart), dist2QTL=ifelse(strand=="-", -1 *dist2QTLnostrand, dist2QTLnostrand), propGene=dist2QTL/geneLength) %>% filter(propGene>-5 & propGene<5)

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
4912eaa	brimittleman	2019-06-14

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
4912eaa	brimittleman	2019-06-14

334 are in the gene body.

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_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   reshape2_1.4.3   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4

Location of apaQTLs

Briana Mittleman

6/14/2019

Distance to PAS

Metagene plot