create_gene_binary: Enables creation of a binary matrix from a mutation, fusion...

In MSKCC-Epi-Bio/gnomeR: Wrangle and analyze IMPACT and TCGA mutation data

Enables creation of a binary matrix from a mutation, fusion or CNA file with a predefined list of samples (rows are samples and columns are genes)

Description

Enables creation of a binary matrix from a mutation, fusion or CNA file with a predefined list of samples (rows are samples and columns are genes)

Usage

create_gene_binary(
  samples = NULL,
  mutation = NULL,
  mut_type = c("omit_germline", "somatic_only", "germline_only", "all"),
  snp_only = FALSE,
  include_silent = FALSE,
  fusion = NULL,
  cna = NULL,
  high_level_cna_only = FALSE,
  specify_panel = "no",
  recode_aliases = "impact"
)

Arguments

samples	a character vector specifying which samples should be included in the resulting data frame. Default is NULL is which case all samples with an alteration in the mutation, cna or fusions file will be used. If you specify a vector of samples that contain samples not in any of the passed genomic data frames, 0's (or NAs when appropriate if specifying a panel) will be returned for every column of that patient row.
mutation	A data frame of mutations in the format of a maf file.
mut_type	The mutation type to be used. Options are "omit_germline", "somatic_only", "germline_only" or "all". Note "all" will keep all mutations regardless of status (not recommended). Default is omit_germline which includes all somatic mutations, as well as any unknown mutation types (most of which are usually somatic)
snp_only	Boolean to rather the genetics events to be kept only to be SNPs (insertions and deletions will be removed). Default is FALSE.
include_silent	Boolean to keep or remove all silent mutations. TRUE keeps, FALSE removes. Default is FALSE.
fusion	A data frame of fusions. If not NULL the outcome will be added to the matrix with columns ending in ".fus". Default is NULL.
cna	A data frame of copy number alterations. If inputed the outcome will be added to the matrix with columns ending in ".del" and ".amp". Default is NULL.
high_level_cna_only	If TRUE, only deep deletions (-2, -1.5) or high level amplifications (2) will be counted as events in the binary matrix. Gains (1) and losses (1) will be ignored. Default is FALSE where all CNA events are counted.
specify_panel	Default is "no" where no panel annotation is done. Otherwise pass a character vector of length 1 with a panel id (see gnomeR::gene_panels for available panels), or "impact" for automated IMPACT annotation. Alternatively, you may pass a data frame of sample_id-panel_id pairs specifying panels for each sample for which to insert NAs indicating genes not tested. See below for details.
recode_aliases	Default is "impact" where function will check for IMPACT genes that may go by more than 1 name in your data and replace the alias name with the standardized gene name (see gnomeR::impact_alias_table for reference list). If "no", no alias annotation will be performed. If "genie", an alias table with GENIE BPC genes will be used to check (see gnomeR::genie_alias_table for reference list). Alternatively, you may pass a custom alias list as a data frame with columns hugo_symbol and alias specifying a custom alias table to use for checks. See below for details.

Value

a data frame with sample_id and alteration binary columns with values of 0/1

specify_panel argument

• If specify_panel = "no" is passed (default) data will be returned as is without any additional NA annotations.

• If a single panel id is passed (e.g. specify_panel = "IMPACT468"), all genes in your data that are not tested on that panel will be set to NA in results for all samples (see gnomeR::gene_panels to see which genes are on each supported panels).

• If specify_panel = "impact" is passed, impact panel version will be inferred based on each sample_id (based on IMX nomenclature) and NA's will be annotated accordingly for each sample/panel pair.

• If you wish to specify different panels for each sample, pass a data frame (with all samples included) with columns: sample_id, and panel_id. Each sample will be annotated with NAs according to that specific panel. If a sample in your data is missing from the sample_id column in the specify_panel dataframe, it will be returned with no annotation (equivalent of setting it to "no").

recode_aliases argument

• If recode_aliases = "impact" is passed (default), function will use gnomeR::impact_alias_table to find and replace any non-standard hugo symbol names with their more common (or more recent) accepted gene name.

• If recode_aliases = "genie" is passed, function will use gnomeR::genie_alias_table to find and replace any non-standard hugo symbol names with their more common (or more recent) accepted gene name.

• If recode_aliases = "no" is passed, data will be returned as is without any alias replacements.

• If you have a custom table of vetted aliases you wish to use, you can pass a data frame with columns: hugo_symbol, and alias. Each row should have one gene in the hugo_symbol column indicating the accepted gene name, and one gene in the alias column indicating an alias you want to check for and replace. If a gene has multiple aliases to check for, each should be represented in its own separate row. See gnomeR::impact_alias_table for an example of accepted data formatting.

Examples

mut.only <- create_gene_binary(mutation = gnomeR::mutations)

samples <- gnomeR::mutations$sampleId

bin.mut <- create_gene_binary(
  samples = samples, mutation = gnomeR::mutations,
  mut_type = "omit_germline", snp_only = FALSE,
  include_silent = FALSE
)

MSKCC-Epi-Bio/gnomeR documentation built on March 28, 2024, 2:42 a.m.