This description applies to each directory. Here I will use thca as an example.
The convert.sh file prepares the text files necessary to create the count and phenotype Robjects.
First, create 3 files
File with absolute path to each count file
ls /group/stranger-lab/grossman_cancer_data/thca/*.gz > thca_files.txt
File provided by Allison to link GDC UUID with TCGA UUID
thca_htseq.txt
File of phenotype data, 3 columns with TCGA_ID, TCGA_UUID, and Gender
Created by:
grep bcr_patient_barcode All_CDEs.txt | tr '\t' '\n' > barcode.txt
grep bcr_patient_uuid All_CDEs.txt | tr '\t' '\n' > uuid.txt
grep gender All_CDEs.txt | tr '\t' '\n' > gender.txt
paste barcode.txt uuid.txt gender.txt > phens.txt
Run the convert.sh script with these input files
sh convert.sh thca_files.txt thca_htseq.txt phens.txt
This creates the id_conversions.txt file read by the expr.R script.
First, create a file with duplicated IDs
cut -f 1 id_conversions.txt | sort | uniq -d > duplicated.txt
This was used previously to remove individuals with both tumor and normal samples.
Run the expr.R script, which filters out duplicated ids, and sorts both the phen and count Robjects to have the same individuals in the same order.
The count_DE.R script takes the previously generated count and phenotype Robjects and runs the DESeq2 sex-biased differential expression analysis.