Last updated: 2019-09-04

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.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
    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.

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File Version Author Date Message
Rmd 5984fad brimittleman 2019-09-04 update for new filter condition
html 8be0515 brimittleman 2019-06-13 Build site.
Rmd ec1023a brimittleman 2019-06-13 fix big bug
html 874081d brimittleman 2019-04-18 Build site.
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/.

  • Cut annotation off:
less ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz |sed  '1d'| cut -f 2- -d " "> ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.Counts

less ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz | cut -f 2- -d " " |sed  '1d'> ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.Counts

Cnnvert to numeric:

python convertNumeric.py ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.Counts  ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.CountsNumeric

python convertNumeric.py ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.Counts  ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.CountsNumeric
less ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz |sed  '1d'| cut -f1 -d " " > ../data/phenotype_5perc/NuclearPeaks5PercId.txt
less ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz |sed  '1d'| cut -f1 -d " " > ../data/phenotype_5perc/TotalPeaks5PercId.txt

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.

gunzip ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz
gunzip ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz
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-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     

loaded via a namespace (and not attached):
 [1] workflowr_1.4.0 Rcpp_1.0.0      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.2 git2r_0.25.2    magrittr_1.5    evaluate_0.12  
 [9] highr_0.7       stringi_1.2.4   fs_1.3.1        whisker_0.3-2  
[13] rmarkdown_1.10  tools_3.5.1     stringr_1.3.1   glue_1.3.0     
[17] yaml_2.2.0      compiler_3.5.1  htmltools_0.3.6 knitr_1.20