To check - Re-run the R report script of RNA-seq and MARS-seq snakemake pipelines

Owned by Refael Kohen (Unlicensed)
Last updated: 12 Nov, 2023 by Dena Leshkowitz
1 min read

After the run of UTAP analysis you can re-run the R report script by command line:

The run of the script will override the original output of the pipeline.


Steps for RNA-seq pipeline:

  1. Save backup of the report folder of the analysis: 4_reports,
    cp -r 4_reports 4_reports.bak
  2. Navigate into the folder of the R script:
    cd 4_reports/report_output_XXXXXXXX_XXXXXX 
  3. Change the report.Rmd script.
  4. Run the command:
    PATH=$PATH:/home/labs/bioservices/services/miniconda2/envs/utap/bin
    /home/labs/bioservices/services/miniconda2/envs/utap/bin/Rscript -e "rmarkdown::render('report.Rmd')" --verbose
  5. The output file is: 4_reports/report_output_XXXXXXXX_XXXXXX/report.html



Steps for MARS-seq pipeline:

  1. Save backup of the report folder of the analysis: 10_reports,
    cp -r 10_reports 10_reports.bak
  2. Navigate into the FIRST folder of the R script (of the analysis without counts correction):
    cd 10_reports/report_output_XXXXXXXX_XXXXXX 
  3. Change the report.Rmd script.
  4. Run the command:

   PATH=$PATH:/home/labs/bioservices/services/miniconda2/envs/utap/bin

   /home/labs/bioservices/services/miniconda2/envs/utap/bin/Rscript -e "rmarkdown::render('report.Rmd')" --verbose

      5. The output file is: 10_reports/report_output_XXXXXXXX_XXXXXX/report.html

      6.Navigate into the SECOND folder of the script (of the analysis with counts correction):
         cd 10_reports/report_umi_counts_output_XXXXXXXX_XXXXXX

      7. Change the report.Rmd script.

      8. Run the command:
          PATH=$PATH:/home/labs/bioservices/services/miniconda2/envs/utap/bin
          /home/labs/bioservices/services/miniconda2/envs/utap/bin/Rscript -e "rmarkdown::render('report.Rmd')" --verbose

      9. The output file is: 10_reports/report_umi_counts_output_XXXXXXXX_XXXXXX/report.html


The parameters in report.Rmd  are (in the start of the script):

correct_by_fdrtool=TRUE#change to FALSE if you don't want to correct p-values by fdrtools
min_coverage <- 5
thresholds <- data_frame(threshold_set = "default", padj=0.05, log2FoldChange=1, max_count=30, baseMean=5)