check_sample_overlap: Catch duplicate samples
In Townsend-Lab-Yale/cancereffectsizeR: Calculate Cancer Effect Size

check_sample_overlap

R Documentation

Catch duplicate samples

Description

Takes in a data.table of MAF data (produced, typically, with preload_maf()) and identifies samples with relatively high proportions of shared SNV mutations. Some flagged sample pairs may reflect shared driver mutations or chance overlap of variants in SNV or sequencing error hotspots. Very high overlap may indicate sample duplication, re-use of samples across data sources, or within-experiment sample contamination. To limit the influence of shared calling error, it's recommended to run this function after any quality filtering of MAF records, as a final step.

Usage

check_sample_overlap(maf_list)

Arguments

maf_list

A list of data.tables (or a single data.table) with MAF data and cancereffectsizeR-style column names, as generated by preload_maf().

Details

Sample pairs are flagged when...

Both samples have <6 total SNVs and any shared SNVs.
Both samples have <21 total SNVs and >1 shared mutation.
One sample has just 1 or 2 total SNVs and has any overlaps with the other sample.
The samples have >2 shared SNVs and at least one percent of SNVs are shared (in the sample with fewer SNVs).

These thresholds err on the side of reporting too many possible duplicates. In general, and especially when dealing with targeted sequencing data, the presence of 1 or 2 shared mutations between a pair of samples is not strong evidence of sample duplication. It's up to the user to filter and interpret the output.

In addition to reporting SNV counts, this function divides the genome into 1000-bp windows and reports the following:

variant_windows_A: Number of windows in which sample A has a variant.
variant_windows_B: Same for B.
windows_shared: Number of windows that contain a variant shared between both samples.

Sometimes, samples have little overlap except for a few hotspots that may derive from shared calling error or highly mutable regions. These window counts can help distinguish such samples from those with more pervasive SNV overlap.