Assign CN to SSM.
assign_cn_to_ssm.RdAnnotate mutations with their copy number information.
Usage
assign_cn_to_ssm(
these_samples_metadata,
maf_data,
seg_data,
projection,
coding_only = FALSE,
assume_diploid = FALSE,
include_silent = FALSE,
...
)Arguments
- these_samples_metadata
Metadata table with one or more rows to specify the samples to process.
- maf_data
A data frame of mutations in MAF format or maf_data object (e.g. from
get_coding_ssmorget_ssm_by_sample).- seg_data
A data frame of segmented copy number data or seg_data object
- projection
Specified genome projection that returned data is relative to. This is only required when it cannot be inferred from maf_df or seg_df (or they are not provided).
- coding_only
Optional. Set to TRUE to restrict to only variants in coding space Default is to work with genome-wide variants.
- assume_diploid
Optional, this parameter annotates every mutation as copy neutral. Default is FALSE.
- include_silent
Logical parameter indicating whether to include silent mutations in coding space. Default is FALSE. This parameter only makes sense if
coding_onlyis set to TRUE.- ...
Any additional parameters.
Value
A list containing a data frame (MAF-like format) with three extra columns: - log.ratio is the log ratio from the seg file (NA when no overlap). - LOH - CN (the rounded absolute copy number estimate of the region based on log.ratio, NA when no overlap was found).
Details
This function takes a metadata table and returns all mutations
for the samples in that metadata. Each mutation is annotated with the
local copy number state of each mutated site. The user can specify if
only coding mutations are of interest. To do so,
set coding_only = TRUE. When necessary, this function relies on
get_ssm_by_samples and get_cn_segments to obtain the required data.
Examples
# long-handed way
# 1. get some metadata for a collection of samples
some_meta = get_gambl_metadata() %>%
dplyr::filter(study=="FL_Dreval",
grepl("SP",sample_id))
#> Using the bundled metadata in GAMBLR.data...
# 2. Get the SSMs for these samples
ssm_genomes_grch37 = get_coding_ssm(projection = "grch37",
these_samples_metadata = some_meta)
#> Using the bundled SSM calls (.maf) calls in GAMBLR.data...
#> after linking with metadata, we have mutations from 182 samples
# peek at the results
ssm_genomes_grch37 %>% dplyr::select(1:8)
#> genomic_data Object
#> Genome Build: grch37
#> Showing first 10 rows:
#> Hugo_Symbol Entrez_Gene_Id Center NCBI_Build Chromosome Start_Position
#> 1 HNRNPU 0 . GRCh37 1 245027102
#> 2 PCLO 0 . GRCh37 7 82784068
#> 3 MYOM2 0 . GRCh37 8 2063762
#> 4 CREBBP 0 . GRCh37 16 3786740
#> 5 IGLL5 0 . GRCh37 22 23230399
#> 6 HIST1H2BC 0 . GRCh37 6 26123774
#> 7 SETD2 0 . GRCh37 3 47088059
#> 8 IRF8 0 . GRCh37 16 85942737
#> 9 ARID5B 0 . GRCh37 10 63661999
#> 10 ARID5B 0 . GRCh37 10 63810677
#> End_Position Strand
#> 1 245027102 +
#> 2 82784068 +
#> 3 2063762 +
#> 4 3786740 +
#> 5 23230400 +
#> 6 26123774 +
#> 7 47088059 +
#> 8 85942737 +
#> 9 63661999 +
#> 10 63810677 +
# 3. Lazily let this function obtain the corresponding seg_data
# for the right genome_build
cn_list = assign_cn_to_ssm(some_meta,ssm_genomes_grch37)
#> Using the bundled CN segments (.seg) calls in GAMBLR.data...
#> Running in default mode of any...
cn_list$maf %>% dplyr::select(1:8,log.ratio,CN)
#> genomic_data Object
#> Genome Build: grch37
#> Showing first 10 rows:
#> Hugo_Symbol Entrez_Gene_Id Center NCBI_Build Chromosome Start_Position
#> 1 HNRNPU 0 . GRCh37 1 245027102
#> 2 PCLO 0 . GRCh37 7 82784068
#> 3 MYOM2 0 . GRCh37 8 2063762
#> 4 CREBBP 0 . GRCh37 16 3786740
#> 5 IGLL5 0 . GRCh37 22 23230399
#> 6 HIST1H2BC 0 . GRCh37 6 26123774
#> 7 SETD2 0 . GRCh37 3 47088059
#> 8 IRF8 0 . GRCh37 16 85942737
#> 9 ARID5B 0 . GRCh37 10 63661999
#> 10 ARID5B 0 . GRCh37 10 63810677
#> End_Position Strand log.ratio CN
#> 1 245027102 + 0.4306932 3
#> 2 82784068 + 0.0000000 2
#> 3 2063762 + 0.0000000 2
#> 4 3786740 + 0.0000000 2
#> 5 23230400 + 0.0000000 2
#> 6 26123774 + 0.0000000 2
#> 7 47088059 + 0.0000000 2
#> 8 85942737 + 1.3219281 5
#> 9 63661999 + 0.5849625 3
#> 10 63810677 + 0.5849625 3
if (FALSE) { # \dontrun{
# This wouldn't work because the hg38 seg_data is not bundled
ssm_genomes_hg38 = get_coding_ssm(projection = "hg38",
these_samples_metadata = some_meta)
cn_list = assign_cn_to_ssm(some_meta,ssm_genomes_hg38)
# Easiest/laziest way:
cn_list = assign_cn_to_ssm(projection = "grch37")
cn_list$maf %>% dplyr::group_by(Tumor_Sample_Barcode,CN) %>%
dplyr::count()
} # }