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. Of note, many options are designed for method "M" only, and they are highlighted by bold fonts (you can ignore them if you don't use "M" method).
sig_tally(object, ...)# S3 method for CopyNumber
sig_tally(
object,
method = "Wang",
ignore_chrs = NULL,
indices = NULL,
add_loh = FALSE,
feature_setting = sigminer::CN.features,
type = c("probability", "count"),
reference_components = FALSE,
cores = 1,
seed = 123456,
min_comp = 2,
max_comp = 15,
min_prior = 0.001,
model_selection = "BIC",
threshold = 0.1,
nrep = 1,
niter = 1000,
keep_only_matrix = FALSE,
...
)
# S3 method for RS
sig_tally(object, keep_only_matrix = FALSE, ...)
# S3 method for MAF
sig_tally(
object,
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 "Macintyre" ("M"), "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).
one of "probability", "count". Default is "probability", return a matrix with the sum of posterior probabilities for each components. If set to 'count', return a matrix with event count assigned to each components. The result for both types should be close. Only used when method is "Macintyre".
default is FALSE, calculate mixture components
from CopyNumber object. Only used when method is "Macintyre".
number of computer cores to run this task.
You can use future::availableCores() function to check how
many cores you can use.
seed number. Only used when method is "Macintyre".
minimal number of components to fit, default is 2. Can also be a vector with length 6, which apply to each feature. Only used when method is "Macintyre".
maximal number of components to fit, default is 15. Can also be a vector with length 6, which apply to each feature. Only used when method is "Macintyre".
the minimum relative size of components, default is 0.001. Details about custom setting please refer to flexmix package. Only used when method is "Macintyre".
model selection strategy, default is 'BIC'. Details about custom setting please refer to flexmix package. Only used when method is "Macintyre".
default is 0.1. Sometimes, the result components
include adjacent distributions with similar mu
(two and more distribution are very close), we use this threshold
to obtain a more meaningful fit with less components.
Only used when method is "Macintyre".
number of run times for each value of component, keep only the solution with maximum likelihood. Only used when method is "Macintyre".
the maximum number of iterations. Only used when method is "Macintyre".
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.
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
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).
Macintyre, Geoff, et al. "Copy number signatures and mutational processes in ovarian carcinoma." Nature genetics 50.9 (2018): 1262.
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
sig_estimate for estimating signature number for sig_extract, sig_auto_extract for extracting signatures using automatic relevance determination technique.
# NOT RUN {
# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
package = "sigminer", mustWork = TRUE
))
# }
# NOT RUN {
# Use method designed by Wang, Shixiang et al.
cn_tally_W <- sig_tally(cn, method = "W")
# Use method designed by Macintyre et al.
cn_tally_M <- sig_tally(cn, method = "M")
# }
# NOT RUN {
# Use method designed by Steele et al.
# See example in read_copynumber
# }
# NOT RUN {
# 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(
laml,
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(
laml,
ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
use_syn = TRUE, add_trans_bias = TRUE
)
## Test it by enrichment analysis
enrich_component_strand_bias(mt_tally$nmf_matrix)
enrich_component_strand_bias(mt_tally$all_matrices$SBS_24)
} else {
message("Please install package 'BSgenome.Hsapiens.UCSC.hg19' firstly!")
}
# }
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