Last updated: 2019-04-29

Checks: 6 0

Knit directory: apaQTL/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.3.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20190411)

The command set.seed(20190411) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: b18b96c

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  .Rprofile
    Untracked:  ._.DS_Store
    Untracked:  .gitignore
    Untracked:  _workflowr.yml
    Untracked:  analysis/._PASdescriptiveplots.Rmd
    Untracked:  analysis/._cuttoffPercUsage.Rmd
    Untracked:  analysis/cuttoffPercUsage.Rmd
    Untracked:  apaQTL.Rproj
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilefiltPAS
    Untracked:  code/._apaQTLCorrectPvalMakeQQ.R
    Untracked:  code/._apaQTL_Nominal.sh
    Untracked:  code/._apaQTL_permuted.sh
    Untracked:  code/._bed2saf.py
    Untracked:  code/._callPeaksYL.py
    Untracked:  code/._chooseAnno2SAF.py
    Untracked:  code/._chooseSignalSite
    Untracked:  code/._chooseSignalSite.py
    Untracked:  code/._cluster.json
    Untracked:  code/._clusterPAS.json
    Untracked:  code/._clusterfiltPAS.json
    Untracked:  code/._config.yaml
    Untracked:  code/._config2.yaml
    Untracked:  code/._configOLD.yaml
    Untracked:  code/._convertNumeric.py
    Untracked:  code/._dag.pdf
    Untracked:  code/._filter5perc.R
    Untracked:  code/._filter5percPheno.py
    Untracked:  code/._filterpeaks.py
    Untracked:  code/._fixFChead.py
    Untracked:  code/._make5percPeakbed.py
    Untracked:  code/._makeFileID.py
    Untracked:  code/._makePheno.py
    Untracked:  code/._mergeAllBam.sh
    Untracked:  code/._mergeByFracBam.sh
    Untracked:  code/._mergePeaks.sh
    Untracked:  code/._namePeaks.py
    Untracked:  code/._peak2PAS.py
    Untracked:  code/._peakFC.sh
    Untracked:  code/._pheno2countonly.R
    Untracked:  code/._quantassign2parsedpeak.py
    Untracked:  code/._snakemakePAS.batch
    Untracked:  code/._snakemakefiltPAS.batch
    Untracked:  code/._submit-snakemakePAS.sh
    Untracked:  code/._submit-snakemakefiltPAS.sh
    Untracked:  code/.snakemake/
    Untracked:  code/APAqtl_nominal.err
    Untracked:  code/APAqtl_nominal.out
    Untracked:  code/APAqtl_permuted.err
    Untracked:  code/APAqtl_permuted.out
    Untracked:  code/BothFracDTPlotGeneRegions.err
    Untracked:  code/BothFracDTPlotGeneRegions.out
    Untracked:  code/DistPAS2Sig.py
    Untracked:  code/README.md
    Untracked:  code/Rplots.pdf
    Untracked:  code/Upstream100Bases_general.py
    Untracked:  code/bam2bw.err
    Untracked:  code/bam2bw.out
    Untracked:  code/dag.pdf
    Untracked:  code/dagPAS.pdf
    Untracked:  code/dagfiltPAS.pdf
    Untracked:  code/findbuginpeaks.R
    Untracked:  code/get100upPAS.py
    Untracked:  code/getSeq100up.sh
    Untracked:  code/getseq100up.err
    Untracked:  code/getseq100up.out
    Untracked:  code/log/
    Untracked:  code/run_DistPAS2Sig.err
    Untracked:  code/run_DistPAS2Sig.out
    Untracked:  code/run_distPAS2Sig.sh
    Untracked:  code/snakePASlog.out
    Untracked:  code/snakefiltPASlog.out
    Untracked:  data/DTmatrix/
    Untracked:  data/PAS/
    Untracked:  data/README.md
    Untracked:  data/SignalSiteFiles/
    Untracked:  data/apaQTLNominal/
    Untracked:  data/apaQTLPermuted/
    Untracked:  data/assignedPeaks/
    Untracked:  data/bam/
    Untracked:  data/bam_clean/
    Untracked:  data/bam_waspfilt/
    Untracked:  data/bed_10up/
    Untracked:  data/bed_clean/
    Untracked:  data/bed_clean_sort/
    Untracked:  data/bed_waspfilter/
    Untracked:  data/bedsort_waspfilter/
    Untracked:  data/fastq/
    Untracked:  data/filterPeaks/
    Untracked:  data/inclusivePeaks/
    Untracked:  data/inclusivePeaks_FC/
    Untracked:  data/mergedBG/
    Untracked:  data/mergedBW_byfrac/
    Untracked:  data/mergedBam/
    Untracked:  data/mergedbyFracBam/
    Untracked:  data/nuc_10up/
    Untracked:  data/nuc_10upclean/
    Untracked:  data/peakCoverage/
    Untracked:  data/peaks_5perc/
    Untracked:  data/phenotype/
    Untracked:  data/phenotype_5perc/
    Untracked:  data/sort/
    Untracked:  data/sort_clean/
    Untracked:  data/sort_waspfilter/
    Untracked:  nohup.out
    Untracked:  output/._.DS_Store
    Untracked:  output/dtPlots/
    Untracked:  output/fastqc/

Unstaged changes:
    Modified:   analysis/PASusageQC.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/bed2saf.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
Rmd b18b96c brimittleman 2019-04-29 fix distance
html 2d33728 brimittleman 2019-04-28 Build site.
Rmd 7416404 brimittleman 2019-04-28 add res
html ed97e35 brimittleman 2019-04-21 Build site.
Rmd be90ded brimittleman 2019-04-21 fix to 5perc phenp
html 28bd046 brimittleman 2019-04-18 Build site.
Rmd 017f5c0 brimittleman 2019-04-18 add map apa qtl pipeline 
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ 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(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(cowplot)

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

    ggsave

In this analysis I will call apaQTls in both fractions. I will start with the phenotype files and normalized the counts using the leafcutter package in order to run the fastq QTL mapper.

Prepare phenotypes for QTL- phenotype dir

It is best to run this analysis in the data/phenotype_5perc directory. I have copied the leafcutter prepare_phenotype_table.py to the code directroy to use here.

#!/bin/bash
module load python

gzip APApeak_Phenotype_GeneLocAnno.Total.5perc.fc
gzip APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc

python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz
python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz

This will output bash scripts to run.

module load Anaconda3
source activate three-prime-env

sh APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz_prepare.sh
sh APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz_prepare.sh

Subset the PCs to use the first 2 in the qtl calling:

module load Anaconda3
source activate three-prime-env

head -n 3 APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.2PCs
head -n 3 APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.2PCs

Call QTLs- code dir

Next I will need to make a sample list. From the code directory:

python makeSampleList.py

remove 19092 and 19193

Prepare directroy

mkdir ../data/apaQTLNominal
mkdir ../data/apaQTLPermuted

Run the code to call QTLs within 1mb of each PAS peak. I run both a nominal pass and a permuted pas. The permulted pas chosses the best snp for each peak gene pair.

sbatch apaQTL_Nominal.sh
sbatch apaQTL_permuted.sh

Concatinate all of the results in the permuted set. I do this so I can account for multiple testing with the benjamini hochberg test.

Concatinate results in permuted directory:

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

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

Run correction script

Rscripts apaQTLCorrectPvalMakeQQ.R

Evaluation results

totRes=read.table("../data/apaQTLPermuted/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] 502

apaQTL genes:

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

summary(TotQTLs_gene$nQTL)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   1.000   1.307   2.000   6.000 
hist(TotQTLs_gene$nQTL)

Version Author Date
2d33728 brimittleman 2019-04-28

Location distribution for peaks:

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


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))
nucRes=read.table("../data/apaQTLPermuted/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] 1070

apaQTL genes:

NucQTLs_gene= NucQTLs %>% group_by(Gene)  %>% summarise(nQTL=n())

summary(NucQTLs_gene$nQTL)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   1.000   1.328   2.000   7.000 
hist(NucQTLs_gene$nQTL)

Version Author Date
2d33728 brimittleman 2019-04-28

Location distribution for peaks:

NucQTLs_loc= NucQTLs %>% group_by(Loc) %>% summarise(nLoc=n()) %>% mutate(PropLoc=nLoc/nrow(NucQTLs))


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

Version Author Date
2d33728 brimittleman 2019-04-28

Distance to PAS

The distance to PAS is the location of the snp to the end of the peak for the

Strand in this file is the peak strand (opposite of gene). This means for + strand I want the start of the Peak and for the - strand i will use the end of the peak.

TotQTLs_dist=TotQTLs %>% separate(sid, into=c("SnpCHR", "SNPpos"), sep=":") %>% mutate(Dist2PAS=ifelse(Strand=="+", as.integer(SNPpos)-as.integer(Start), as.integer(SNPpos)-as.integer(End)))


summary(abs(TotQTLs_dist$Dist2PAS))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0    1598    6160    8832   15461   25071

Plot:

ggplot(TotQTLs_dist, aes(x=Dist2PAS)) + geom_histogram(bins=100) + theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggplot(TotQTLs_dist, aes(x=Dist2PAS)) + geom_histogram(bins=100) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + facet_grid(~Loc)

```


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   workflowr_1.3.0 reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.0   tidyverse_1.2.1

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_0.23.0     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   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4