Classify DLBCLs according to genetic subgroups of Lacy et al. — classify_dlbcl

Use the random forest model to classify DLBCL tumors based on system of Lacy et al

Usage

classify_dlbcl_lacy(
  these_samples_metadata,
  maf_data,
  seg_data,
  sv_data,
  projection = "grch37",
  output = "both",
  include_N1 = FALSE
)

these_samples_metadata: The metadata data frame that contains sample_id column with ids for the samples to be classified.
maf_data: The MAF data frame to be used for matrix assembling. At least must contain the first 45 columns of standard MAF format.
seg_data: The SEG data frame to be used for matrix assembling. Must be of standard SEG formatting, for example, as returned by get_cn_segments.
sv_data: The SV data frame to be used for matrix assembling. Must be of standard BEDPE formatting, for example, as returned by get_combined_sv.
projection: The projection of the samples. Used to annotate hotspot SSM mutations and retreive coordinates for shm features. Defaults to grch37.
output: The output to be returned after prediction is done. Can be one of predictions, matrix, or both. Defaults to both.
include_N1: Whether to set samples with NOTCH1 truncating mutations to N1 group as described in Runge et al (2021). Defaults to FALSE.

data frame, binary matrix, or both