add_variants: Add variant annotations

View source: R/add_variants.R

add_variantsR Documentation

Add variant annotations

Description

Use this function to add variant annotations to your CESAnalysis by specifying variants to add in one of five ways: a data.table containing genomic coordinates (output from select_variants(), typically), a GRanges object, a BED file, another CESAnalysis, or SNV/AAC IDs.

Usage

add_variants(
  target_cesa = NULL,
  variant_table = NULL,
  snv_id = NULL,
  aac_id = NULL,
  bed = NULL,
  gr = NULL,
  source_cesa = NULL,
  padding = 0
)

Arguments

target_cesa

CESAnalysis to receive variant annotations

variant_table

A data.table with chr/start/end positions (1-based closed coordinates, like MAF format). All possible SNVs overlapping the table's genomic coordinates (within padding bases) will be added. The tables returned by select_variants() and (CESAnalysis)$variants work, and get special handling of amino-acid-change SNVs: only the precise positions in start, end, and center_nt_pos are used. (This avoids adding all variants between start/end, which on splice-site-spanning variants can be many thousands.)

snv_id

Character vector of CES-style SNV IDs to add.

aac_id

Character vector of AAC IDs (or short names, like "KRAS_G12C")

bed

A path to a BED file. All possible SNVs overlapping BED intervals (within padding bases) will be added.

gr

A GRanges object. All possible SNVs overlapping the ranges (within padding bases) will be added.

source_cesa

Another CESAnalysis from which to copy snv_ids. SNVs will be re-annotated using the target_cesa's associated reference data.

padding

How many bases (default 0) to expand start and end of each gr range

Details

All methods of adding variants work by identifying which SNVs to add and then using the target_cesa's associated reference data to identify overlapping amino-acid-change mutations, which are then added as well. (You can't add just SNVs or just AACs.) Note that if you try to add far more distinct variants than appear in a typical cohort (as in, millions), annotation will take a while and the annotation tables in the CESAnalysis may take up significant memory. Please contact us if you have issues.


Townsend-Lab-Yale/cancereffectsizeR documentation built on April 28, 2024, 6:14 p.m.