get_ssm_by_patients.RdGet MAF-format data frame for more than one patient.
get_ssm_by_patients(
these_patient_ids,
these_samples_metadata,
tool_name = "slms-3",
projection = "grch37",
seq_type = "genome",
flavour = "clustered",
min_read_support = 3,
basic_columns = TRUE,
maf_cols = NULL,
subset_from_merge = FALSE,
augmented = TRUE,
engine = "fread_maf"
)A vector of patient IDs that you want results for. The user can also use a metadata table that has been subset to the patient IDs of interest (these_samples_metadata).
A metadata subset to contain the rows corresponding to the patients of interest. If the vector of patient IDs is missing (these_patient_ids), this function will default to all patient IDs in the metadata table given to this parameter.
Only supports slms-3 currently.
Obtain variants projected to this reference (one of grch37 or hg38).
The seq type you want results for. Default is "genome".
Currently this function only supports one flavour option but this feature is meant for eventual compatibility with additional variant calling parameters/versions.
Only returns variants with at least this many reads in t_alt_count (for cleaning up augmented MAFs).
Return first 43 columns of MAF rather than full details. Default is TRUE.
if basic_columns is set to FALSE, the user can specify what columns to be returned within the MAF. This parameter can either be a vector of indexes (integer) or a vector of characters (matching columns in MAF).
Instead of merging individual MAFs, the data will be subset from a pre-merged MAF of samples with the specified seq_type.
default: TRUE. Set to FALSE if you instead want the original MAF from each sample for multi-sample patients instead.
Specify one of readr or fread_maf (default) to change how the large files are loaded prior to subsetting. You may have better performance with one or the other but for me fread_maf is faster and uses a lot less RAM.
A data frame with SSM calls for the selected patients in MAF format.
This function returns variants from a set of patients avoiding duplicated mutations from multiple samples from that patient (i.e. unique superset of variants).
This is done either by combining the contents of individual MAF files or subset from a merged MAF (wraps get_ssm_by_samples).
In most situations, this should never need to be run with subset_from_merge = TRUE. Instead use one of get_coding_ssm or get_ssm_by_region.
This function expects either a vector of patient IDs (thse_patients_ids) or an already subset metadata table (these_samples_metadata).
Is this function not what you are looking for? Try one of the following, similar, functions; get_coding_ssm, get_coding_ssm_status,
get_ssm_by_sample, get_ssm_by_samples, get_ssm_by_region, get_ssm_by_regions
library(parallel)
#example 1, using a vector of patient IDs.
patients = c("00-14595", "00-15201", "01-12047")
patients_maf = get_ssm_by_patients(these_patient_ids = patients,
seq_type = "genome",
subset_from_merge = FALSE)
#> WARNING: on-the-fly merges can be extremely slow and consume a lot of memory. Use at your own risk.
#> WARNING: on-the-fly merges can be extremely slow and consume a lot of memory if many samples are involved. Use at your own risk.
#example 2, using a metadata table, subset to the patient IDs of interest.
patient_meta = get_gambl_metadata(seq_type_filter = "genome")
patient_meta = dplyr::filter(patient_meta, patient_id %in% patients)
patients_maf_2 = get_ssm_by_patients(these_samples_metadata = patient_meta,
subset_from_merge = FALSE)
#> WARNING: on-the-fly merges can be extremely slow and consume a lot of memory. Use at your own risk.
#> WARNING: on-the-fly merges can be extremely slow and consume a lot of memory if many samples are involved. Use at your own risk.