Last updated: 2019-04-18

Checks: 6 0

Knit directory: apaQTL/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report 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(12345)

The command set.seed(12345) 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: 22bfb66

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:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  .gitignore
    Untracked:  apaQTL.Rproj
    Untracked:  docs/.DS_Store
    Untracked:  docs/MetaDataSequencing.xlsx
    Untracked:  docs/~$MetaDataSequencing.xlsx

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 22bfb66 brimittleman 2019-04-18 add pas usage qc analysis

In this analysis I will test the avrage peak usage difference in peak usage between individuals. We expect the average difference to as you look at peaks with more coverage. I will use this analysis to test for potential batch effects. Prior to resequencing I saw higher variance in the last batch of samples. This was highly correlated with the library concentrations of these samples being lower.I reran each of these libraries for this round.

For interpretation purposes, I look at genes with only 2 peaks. I will also need the metadata to order the plot.

For simplicity I will do this seperatly for total and nuclear. The files are in /project2/gilad/briana/apaQTL/data/phenotype/ I will need to join the first column of the fc file with the countsonly numeric file. I can filter the 5% peaks by joining these files with the file in /project2/gilad/briana/apaQTL/data/phenotype_5perc. To partition this information by count I will also use the counts file and filter by the 5% usage peaks. The counts are in /project2/gilad/briana/apaQTL/data/peakCoverage/.

I will do this in an R script that can be run from the code directory.

The R script will take either Total and Nuclear.

module load Anaconda3
source activate three-prime-env
Rscript UsageDifferenceHeatmap.R -F Total
Rscript UsageDifferenceHeatmap.R -F Nuclear
The resulting plots are written to output as AverageDiffHeatmap.Nuclear.png and AverageDiffHeatmap.Total.png


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.2.0 Rcpp_1.0.0      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.3 git2r_0.24.0    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.3.1   fs_1.2.6        whisker_0.3-2   rmarkdown_1.11 
[13] tools_3.5.1     stringr_1.4.0   glue_1.3.0      xfun_0.5       
[17] yaml_2.2.0      compiler_3.5.1  htmltools_0.3.6 knitr_1.21