Last updated: 2019-04-30

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.


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.

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.

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.

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

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

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/._aAPAqtl_nominal39ind.sh
    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/._extractGenotypes.py
    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/._selectNominalPvalues.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_nominal_39.err
    Untracked:  code/APAqtl_nominal_39.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/aAPAqtl_nominal39ind.sh
    Untracked:  code/bam2bw.err
    Untracked:  code/bam2bw.out
    Untracked:  code/dag.pdf
    Untracked:  code/dagPAS.pdf
    Untracked:  code/dagfiltPAS.pdf
    Untracked:  code/extractGenotypes.py
    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/selectNominalPvalues.py
    Untracked:  code/snakePASlog.out
    Untracked:  code/snakefiltPASlog.out
    Untracked:  data/DTmatrix/
    Untracked:  data/PAS/
    Untracked:  data/QTLGenotypes/
    Untracked:  data/README.md
    Untracked:  data/SignalSiteFiles/
    Untracked:  data/ThirtyNineIndQtl_nominal/
    Untracked:  data/apaQTLNominal/
    Untracked:  data/apaQTLPermuted/
    Untracked:  data/apaQTLs/
    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
    Modified:   analysis/newQTLheatmap.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 eb0bd95 brimittleman 2019-04-30 add write out step
html ac656aa brimittleman 2019-04-30 Build site.
Rmd f9b8195 brimittleman 2019-04-30 understand usage of new pas

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

These results have 30k more PAS than the previous runs. I also see a confusing shift in mean usage for all of the PAS. I want to compare the distribution of usage for different sets of individuals to see if there is something inherently different about the 15 new individuals.

New vs old peaks

I want to compare the usage of the new peaks compared to the overall mean usage. To do this I need to seperate the new and old PAS.

newPAS5perc=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, col.names = c("chr", "start","end", "ID", "score", "strand"))
oldPAS5perc=read.table("../../threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed", stringsAsFactors = F, col.names = c("chr", "start", "end", "ID", "score", "strand"))

uniqnew=newPAS5perc %>% semi_join(oldPAS5perc, by=c("chr", "start", "end"))

Pull in the usage of the peaks:

Total

totalPeakUs=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "loc", "strand", "peak"))
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
ind=colnames(totalPeakUs)[8:dim(totalPeakUs)[2]]
totalPeakUs_CountNum=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.CountsOnlyNumeric", col.names = ind)


#numeric with anno
totalPeak=as.data.frame(cbind(totalPeakUs[,1:7], totalPeakUs_CountNum))

totalPeakUs_CountNum_mean=rowMeans(totalPeakUs_CountNum)

#append mean to anno
TotalPeakUSMean=as.data.frame(cbind(totalPeakUs[,1:7],mean=totalPeakUs_CountNum_mean))
uniqnewPasnum=uniqnew  %>% separate(ID ,into=c("peaknum", "geneloc"),sep=":") %>% mutate(peak=paste("peak", peaknum, sep="")) %>% select(peak)

Filter these inthe mean usage:

TotalPeakUSMeanClass= TotalPeakUSMean %>% mutate(New=ifelse(peak %in% uniqnewPasnum$peak,"new", "original")) %>% mutate(Cutoff=ifelse(mean>=.05, "Yes", "No"))

mean(TotalPeakUSMean$mean)
[1] 0.2378282

Plot:

ggplot(TotalPeakUSMeanClass, aes(y=mean,x=New)) + geom_violin() + geom_hline(yintercept = mean(TotalPeakUSMean$mean), col="red") + facet_grid(~Cutoff)

Version Author Date
ac656aa brimittleman 2019-04-30

This shows me the new peaks are not the peaks that barely passed the cuttoff before. These peaks cover the distribution of usage.

write out file with information about new and old peaks

Peak_newOld=TotalPeakUSMeanClass %>% select(-mean)
write.table(Peak_newOld, file="../data/peaks_5perc/NewVOldPeaks.txt", col.names = T, row.names = F, quote=F)

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] workflowr_1.3.0 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_3.1.0   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   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