Get SSM By Samples.
get_ssm_by_samples.RdGet the SSMs (i.e. load MAF) for a single sample or a collection of samples.
Usage
get_ssm_by_samples(
these_sample_ids = NULL,
these_samples_metadata = NULL,
this_seq_type = "genome",
projection = "grch37",
tool_name = "slms-3",
verbose = FALSE,
...
)Arguments
- these_sample_ids
A vector of one or more sample IDs that you want results for.
- these_samples_metadata
Optional, a metadata table (with sample_id column) to auto-subset the data to samples in that table before returning. If not provided and these_sample_ids is also not provided, the function will return SSM for all samples from the specified seq_type in the bundled metadata.
- this_seq_type
Default is genome.
- projection
The projection genome build. Supports hg38 and grch37.
- tool_name
Optionally specify which tool to report variant from. The default is slms-3, also supports "publication" to return the exact variants as reported in the original papers.
- verbose
Enable for debugging/noisier output.
- ...
Any additional parameters.
Details
Retrieve a maf for a specific sample or a set of samples.
Either specify the sample IDs of interest with these_sample_ids.
Or a metadata table subset to the sample IDs of interest with
these_samples_metadata.
Examples
#Get genome-wide set of mutations from all DLBCL cell lines
# 1. get our metadata for the DLBCL cell lines
cell_line_meta = get_gambl_metadata() %>%
dplyr::filter(cohort == "DLBCL_cell_lines")
#> Using the bundled metadata in GAMBLR.data...
# 2. get the SSMs for the DLBCL cell lines
dlbcl_maf = get_ssm_by_samples(these_samples_metadata = cell_line_meta)
#> Using the bundled SSM calls (.maf) calls in GAMBLR.data...
# 3. have a look:
dlbcl_maf %>% dplyr::group_by(Tumor_Sample_Barcode) %>%
dplyr::count()
#> genomic_data Object
#> Genome Build: grch37
#> Showing first 10 rows:
#> Tumor_Sample_Barcode n
#> 1 DOHH-2 22089
#> 2 OCI-Ly10 30051
#> 3 OCI-Ly3 31532
#> 4 SU-DHL-10 26855
#> 5 SU-DHL-4 32824