Merge branch 'master' of https://github.com/kkmann/impute-gene-expression

README.md

-# Impute gene expression data for CENTER-TBI using PrediXcan
+# Impute gene expression for CENTER-TBI with PrediXcan
 
-The singularity container is available for download under https://doi.org/10.5281/zenodo.3376504. 
-Data currently needs to be accessed manually due to access restrictions, this workflow should work for enssentially any
-vcf.gz file with dosage (DS) information.
-
-More information on PrediXcan can be found here https://github.com/hakyimlab/PrediXcan and here in the publication:
+The singularity container with most software dpendencies is available at 
+https://doi.org/10.5281/zenodo.3376504. 
+Data currently needs to be accessed manually due to access restrictions.
+This workflow is design for *.vcf.gz files with dosage (DS) information.
 
+More information on PrediXcan can be found here https://github.com/hakyimlab/PrediXcan and in:
 > Gamazon ER†, Wheeler HE†, Shah KP†, Mozaffari SV, Aquino-Michaels K, Carroll RJ, Eyler AE, Denny JC, 
 Nicolae DL, Cox NJ, Im HK. (2015) A gene-based association method for mapping traits using reference transcriptome data.
 Nat Genet. doi:10.1038/ng.3367.
 
-We use snakemake to organize the workflow (also pre-installed in the container) and support cluster execution.
-> Johannes Köster, Sven Rahmann, Snakemake—a scalable bioinformatics workflow engine, Bioinformatics, 
-Volume 28, Issue 19, 1 October 2012, Pages 2520–2522, https://doi.org/10.1093/bioinformatics/bts480
-
 ## Dependencies
 
 1. linux shell (`bash`), possibly via virtual machine on Windows/Mac 
 2. `wget` (pre-installed or via distribution package manager)
-3. `singularity` container software (tested on 3.3.0) https://sylabs.io/guides/3.3/user-guide
-4. `git`
+3. `singularity` container software (tested on 3.3.0, https://sylabs.io/guides/3.3/user-guide)
+4. `git` (https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 
 ### Optional
 
 5. python 3.7+ and snakemake 
 6. slurm cluster
 
+We use snakemake to organize the workflow (also pre-installed in the container) and support cluster execution.
+Snakemake is available via `pip` package for python 3.7.
+> Johannes Köster, Sven Rahmann, Snakemake—a scalable bioinformatics workflow engine, Bioinformatics, 
+Volume 28, Issue 19, 1 October 2012, Pages 2520–2522, https://doi.org/10.1093/bioinformatics/bts480
+
+
 ## Execution
  
 Download and extract the contents of this repository (might be access restricted)
@@ -46,4 +48,14 @@ Optionally, if snakemake is installed, the workflow can be run in parallel via
     snakemake --use-singularity -j 8 impute
     
 where '8' can be replaced by the number of available cores.
+Cluster execution is enables via the `scripts/slurm_snakemake.sh` script as
+
+    bash scripts/slurm_snakemake.sh impute
+    
+    
+## Results
 
+Intermediate files (PrediXcan dosage files and raw outputs) are stored in the `outputs/` 
+subfolder of the working directory.
+The file `output/gene_expressions_combined.rds` combines imputed gene expression levels across 
+all available brain regions in a compressed .rds file  (R data set).