Remap qtls with 4 pcs

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
    Modified:   docs/figure/QTLlocation.Rmd/figure2B-1.pdf
    Modified:   docs/figure/QTLlocation.Rmd/figure2C-1.pdf
    Modified:   docs/figure/nonNormQTL.Rmd/figure2D-1.pdf
    Deleted:    reads_graphs.Rmd

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
Rmd	73f5833	brimittleman	2019-09-04	wflow_publish(c(“analysis/QTLlocation.Rmd”, “analysis/apaQTLoverlap.Rmd”, “analysis/nonNormQTL.Rmd”,
html	6745e4c	brimittleman	2019-07-09	Build site.
Rmd	30a2657	brimittleman	2019-07-09	change plot lables
html	3cddcab	brimittleman	2019-07-03	Build site.
Rmd	e1201a2	brimittleman	2019-07-03	add prop qtl of tested
html	6dae9b1	brimittleman	2019-06-13	Build site.
html	1a29304	brimittleman	2019-06-11	Build site.
Rmd	8cbea22	brimittleman	2019-06-11	change genotypes
html	84c56ab	brimittleman	2019-05-09	Build site.
Rmd	d63b37f	brimittleman	2019-05-09	results
html	144c00b	brimittleman	2019-05-08	Build site.
Rmd	5e39f1c	brimittleman	2019-05-08	choose pcs and start qtl rerun

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

Run QTL script

head -n 5 APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.4PCs
head -n 5 APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.4PCs

add batch to these:

metadata=read.table("../data/MetaDataSequencing.txt",stringsAsFactors = F,header = T)  %>% filter(fraction=="total") %>% dplyr::select(line,batch)

pc_tot=as.data.frame(read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.4PCs",header=T) %>%  dplyr::select(-id) %>% t() )%>% rownames_to_column(var="line")

pc_tot_batch=as.data.frame(pc_tot %>% full_join(metadata,by="line") %>% t()) %>% mutate()
colnames(pc_tot_batch)=unname(unlist(pc_tot_batch[1,])) 
pc_tot_batch= pc_tot_batch[2:nrow(pc_tot_batch),] %>% mutate(id=c("1", "2", "3", "4", "batch")) %>%  dplyr::select(id, contains("NA"))


write.table(pc_tot_batch,file="../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.4PCswithBatch", col.names = T, row.names = F, quote=F,sep="\t")

pc_nuc=as.data.frame(read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.4PCs",header=T) %>%  dplyr::select(-id) %>% t() )%>% rownames_to_column(var="line")

pc_nuc_batch=as.data.frame(pc_nuc %>% full_join(metadata,by="line") %>% t()) %>% mutate()
colnames(pc_nuc_batch)=unname(unlist(pc_nuc_batch[1,])) 
pc_nuc_batch= pc_nuc_batch[2:nrow(pc_nuc_batch),] %>% mutate(id=c("1", "2", "3", "4", "batch")) %>%  dplyr::select(id, contains("NA"))


write.table(pc_nuc_batch,file="../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.4PCswithBatch", col.names = T, row.names = F, quote=F,sep = "\t")

mkdir ../data/apaQTLNominal_4pc
mkdir ../data/apaQTLPermuted_4pc

sbatch apaQTL_Nominal_4pc.sh
sbatch apaQTL_permuted.4pc.sh

Concatinate results in permuted directory:

cat ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_chr* > ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total_permRes.txt

cat ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr* > ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permRes.txt

Run correction script

Rscript apaQTLCorrectPvalMakeQQ_4pc.R

Evaluate results:

Total

totRes=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")

Total Apa QTLs

TotQTLs= totRes %>% filter(-log10(bh)>=1)
nrow(TotQTLs)

[1] 443

apaQTL genes:

TotQTLs_gene=TotQTLs %>% group_by(Gene)  %>% summarise(nQTL=n())

summary(TotQTLs_gene$nQTL)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   1.000   1.255   1.000   4.000

hist(TotQTLs_gene$nQTL)

Version	Author	Date
6dae9b1	brimittleman	2019-06-13
1a29304	brimittleman	2019-06-11
84c56ab	brimittleman	2019-05-09

Location distribution for peaks:

TotQTLs_loc= TotQTLs %>% group_by(Loc) %>% summarise(nLoc=n()) %>% mutate(PropLoc=nLoc/nrow(TotQTLs)) %>% mutate(fraction="Total")


totQTLloc=ggplot(TotQTLs_loc, aes(x=Loc, y=PropLoc, fill=Loc)) + geom_bar(stat = "Identity") + labs(x="Location of Significant Peak", y="Proportion of QTLs", title="Total QTL peak distribution")+ theme(axis.text.x = element_text(angle = 90, hjust = 1))

Nucelar:

nucRes=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")

Nuclear Apa QTLs

NucQTLs= nucRes %>% filter(-log10(bh)>=1)
nrow(NucQTLs)

[1] 602