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tcga

TCGA pipeline: (pan)cancer stats.

A series of python scripts to download select pieces of TCGA, store them as a local MySql database and extract various statistics.

Its current use is as a prep step for merging with icgc pipeline. The only two subdirs remaining in use are 00_common_tasks and 01_somatic_mutations.

Common tasks

'Common tasks' refer to tasks needed to make a functional local subset of TCGA. The only non-obsolete piece remaining is 200_find_maf_files_in_GDC.py that can be used to download somatic mutation tables from GDC - a repository of legacy TCGA data.

Compiling somatic mutations

Creating MySQL tables

001_drop_maf_tables though 002_create_maf_tables

Reading in and cleaning up the data

003_maf_meta through 012_drop_annotation

'Stuttering' samples

Some samples in TCGA have serious problems with assembly or data interpretation. Example:

  broad.mit.edu_LIHC.IlluminaGA_DNASeq_automated.Level_2.1.0.0/
  An_TCGA_LIHC_External_capture_All_Pairs.aggregated.capture.tcga.uuid.curated.somatic.maf 
           273933        RPL5       Frame_Shift_Del         p.K270fs 
           273933        RPL5       Frame_Shift_Del         p.K277fs 
           273933        RPL5       Frame_Shift_Del         p.R279fs 
           273933        RPL5       Frame_Shift_Del         p.Q282fs

Such samples stand out pretty sharply and here we detect them as having two frameshift mutations within 5 nucleotides from each other reported more than a 100 times. Such samples are marked in 003_maf_meta.py. Later we decided to drop them in 014_drop_stuttering_samples.py

After this point we can move to ICGC - TCGA data will be fused into the combined dataset over there.

Some basic stats

... provided by 020_db_stats.py through 027_patient_freqs.py.