Tally a variation object like MAF, CopyNumber and return a matrix for NMF de-composition and more. This is a generic function, so it can be further extended to other mutation cases. Please read details about how to set sex for identifying copy number signatures. Please read https://osf.io/s93d5/ for the generation of SBS, DBS and ID (INDEL) components.

sig_tally(object, ...)

# S3 method for CopyNumber
  method = "Wang",
  ignore_chrs = NULL,
  indices = NULL,
  add_loh = FALSE,
  feature_setting = sigminer::CN.features,
  cores = 1,
  keep_only_matrix = FALSE,

# S3 method for RS
sig_tally(object, keep_only_matrix = FALSE, ...)

# S3 method for MAF
  mode = c("SBS", "DBS", "ID", "ALL"),
  ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
  genome_build = NULL,
  add_trans_bias = FALSE,
  ignore_chrs = NULL,
  use_syn = TRUE,
  keep_only_matrix = FALSE,



a CopyNumber object or MAF object or SV object (from read_sv_as_rs).


custom setting for operating object. Detail see S3 method for corresponding class (e.g. CopyNumber).


method for feature classification, can be one of "Wang" ("W"), "S" (for method described in Steele et al. 2019).


Chromsomes to ignore from analysis. e.g. chrX and chrY.


integer vector indicating segments to keep.


flag to add LOH classifications.


a data.frame used for classification. Only used when method is "Wang" ("W"). Default is CN.features. Users can also set custom input with "feature", "min" and "max" columns available. Valid features can be printed by unique(CN.features$feature).


number of computer cores to run this task. You can use future::availableCores() function to check how many cores you can use.


if TRUE, keep only matrix for signature extraction. For a MAF object, this will just return the most useful matrix.


type of mutation matrix to extract, can be one of 'SBS', 'DBS' and 'ID'.


'BSgenome.Hsapiens.UCSC.hg19', 'BSgenome.Hsapiens.UCSC.hg38' and 'BSgenome.Mmusculus.UCSC.mm10' etc.


genome build 'hg19', 'hg38' or "mm10", if not set, guess it by ref_genome.


if TRUE, consider transcriptional bias categories. 'T:' for Transcribed (the variant is on the transcribed strand); 'U:' for Un-transcribed (the variant is on the untranscribed strand); 'B:' for Bi-directional (the variant is on both strand and is transcribed either way); 'N:' for Non-transcribed (the variant is in a non-coding region and is untranslated); 'Q:' for Questionable. NOTE: the result counts of 'B' and 'N' labels are a little different from SigProfilerMatrixGenerator, the reason is unknown (may be caused by annotation file).


Logical. If TRUE, include synonymous variants in analysis.


a list contains a matrix used for NMF de-composition.


For identifying copy number signatures, we have to derive copy number features firstly. Due to the difference of copy number values in sex chromosomes between male and female, we have to do an extra step if we don't want to ignore them.

I create two options to control this, the default values are shown as the following, you can use the same way to set (per R session).

options(sigminer.sex = "female", sigminer.copynumber.max = NA_integer_)

  • If your cohort are all females, you can totally ignore this.

  • If your cohort are all males, set sigminer.sex to 'male' and sigminer.copynumber.max to a proper value (the best is consistent with read_copynumber).

  • If your cohort contains both males and females, set sigminer.sex as a data.frame with two columns "sample" and "sex". And set sigminer.copynumber.max to a proper value (the best is consistent with read_copynumber).

Methods (by class)

  • CopyNumber: Returns copy number features, components and component-by-sample matrix

  • RS: Returns genome rearrangement sample-by-component matrix

  • MAF: Returns SBS mutation sample-by-component matrix and APOBEC enrichment


Wang, Shixiang, et al. "Copy number signature analyses in prostate cancer reveal distinct etiologies and clinical outcomes." medRxiv (2020).

Steele, Christopher D., et al. "Undifferentiated sarcomas develop through distinct evolutionary pathways." Cancer Cell 35.3 (2019): 441-456.

Mayakonda, Anand, et al. "Maftools: efficient and comprehensive analysis of somatic variants in cancer." Genome research 28.11 (2018): 1747-1756.

Roberts SA, Lawrence MS, Klimczak LJ, et al. An APOBEC Cytidine Deaminase Mutagenesis Pattern is Widespread in Human Cancers. Nature genetics. 2013;45(9):970-976. doi:10.1038/ng.2702.

Bergstrom EN, Huang MN, Mahto U, Barnes M, Stratton MR, Rozen SG, Alexandrov LB: SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 2019, 20:685 https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6041-2

See also

sig_estimate for estimating signature number for sig_extract, sig_auto_extract for extracting signatures using automatic relevance determination technique.


Shixiang Wang


# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
# \donttest{
# Use method designed by Wang, Shixiang et al.
cn_tally_W <- sig_tally(cn, method = "W")
# }
# Use method designed by Steele et al.
# See example in read_copynumber
# \donttest{
# Prepare SBS signature analysis
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read_maf(maf = laml.maf)
if (require("BSgenome.Hsapiens.UCSC.hg19")) {
  mt_tally <- sig_tally(
    ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
    use_syn = TRUE
  mt_tally$nmf_matrix[1:5, 1:5]

  ## Use strand bias categories
  mt_tally <- sig_tally(
    ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
    use_syn = TRUE, add_trans_bias = TRUE
  ## Test it by enrichment analysis
} else {
  message("Please install package 'BSgenome.Hsapiens.UCSC.hg19' firstly!")
# }