The function performs a signatures decomposition of a given mutational
catalogue V
with known signatures W
by solving the minimization problem
min(||W*H - V||)
where W and V are known.
sig_fit( catalogue_matrix, sig, sig_index = NULL, sig_db = c("legacy", "SBS", "DBS", "ID", "TSB", "SBS_Nik_lab", "RS_Nik_lab", "RS_BRCA560", "RS_USARC", "CNS_USARC", "SBS_hg19", "SBS_hg38", "SBS_mm9", "SBS_mm10", "DBS_hg19", "DBS_hg38", "DBS_mm9", "DBS_mm10", "SBS_Nik_lab_Organ", "RS_Nik_lab_Organ", "latest_SBS_GRCh37", "latest_DBS_GRCh37", "latest_ID_GRCh37", "latest_SBS_GRCh38", "latest_DBS_GRCh38", "latest_SBS_mm9", "latest_DBS_mm9", "latest_SBS_mm10", "latest_DBS_mm10", "latest_SBS_rn6", "latest_DBS_rn6"), db_type = c("", "human-exome", "human-genome"), show_index = TRUE, method = c("QP", "NNLS", "SA"), auto_reduce = FALSE, type = c("absolute", "relative"), return_class = c("matrix", "data.table"), return_error = FALSE, rel_threshold = 0, mode = c("SBS", "DBS", "ID", "copynumber"), true_catalog = NULL, ... )
catalogue_matrix | a numeric matrix |
---|---|
sig | a |
sig_index | a vector for signature index. "ALL" for all signatures. |
sig_db | default 'legacy', it can be 'legacy' (for COSMIC v2 'SBS'),
'SBS', 'DBS', 'ID' and 'TSB' (for COSMIV v3.1 signatures).
For more specific details, it can also be 'SBS_hg19', 'SBS_hg38',
'SBS_mm9', 'SBS_mm10', 'DBS_hg19', 'DBS_hg38', 'DBS_mm9', 'DBS_mm10' to use
COSMIC v3 reference signatures from Alexandrov, Ludmil B., et al. (2020) (reference #1).
In addition, it can be one of "SBS_Nik_lab_Organ", "RS_Nik_lab_Organ",
"SBS_Nik_lab", "RS_Nik_lab" to refer reference signatures from
Degasperi, Andrea, et al. (2020) (reference #2).
UPDATE, the latest version of reference version can be automatically
downloaded and loaded from https://cancer.sanger.ac.uk/signatures/downloads/
when a option with |
db_type | only used when |
show_index | if |
method | method to solve the minimazation problem. 'NNLS' for non-negative least square; 'QP' for quadratic programming; 'SA' for simulated annealing. |
auto_reduce | if |
type | 'absolute' for signature exposure and 'relative' for signature relative exposure. |
return_class | string, 'matrix' or 'data.table'. |
return_error | if |
rel_threshold | numeric vector, a signature with relative exposure
lower than (equal is included, i.e. |
mode | signature type for plotting, now supports 'copynumber', 'SBS', 'DBS', 'ID' and 'RS' (genome rearrangement signature). |
true_catalog | used by sig_fit_bootstrap, user never use it. |
... | control parameters passing to argument |
The exposure result either in matrix
or data.table
format.
If return_error
set TRUE
, a list
is returned.
The method 'NNLS' solves the minimization problem with nonnegative least-squares constraints. The method 'QP' and 'SA' are modified from SignatureEstimation package. See references for details. Of note, when fitting exposures for copy number signatures, only components of feature CN is used.
Daniel Huebschmann, Zuguang Gu and Matthias Schlesner (2019). YAPSA: Yet Another Package for Signature Analysis. R package version 1.12.0.
Huang X, Wojtowicz D, Przytycka TM. Detecting presence of mutational signatures in cancer with confidence. Bioinformatics. 2018;34(2):330–337. doi:10.1093/bioinformatics/btx604
Kim, Jaegil, et al. "Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors." Nature genetics 48.6 (2016): 600.
W <- matrix(c(1, 2, 3, 4, 5, 6), ncol = 2) colnames(W) <- c("sig1", "sig2") W <- apply(W, 2, function(x) x / sum(x)) H <- matrix(c(2, 5, 3, 6, 1, 9, 1, 2), ncol = 4) colnames(H) <- paste0("samp", 1:4) V <- W %*% H V if (requireNamespace("quadprog", quietly = TRUE)) { H_infer <- sig_fit(V, W, method = "QP") H_infer H H_dt <- sig_fit(V, W, method = "QP", auto_reduce = TRUE, return_class = "data.table") H_dt ## Show results show_sig_fit(H_infer) show_sig_fit(H_dt) ## Get clusters/groups H_dt_rel <- sig_fit(V, W, return_class = "data.table", type = "relative") z <- get_groups(H_dt_rel, method = "k-means") show_groups(z) } # if (requireNamespace("GenSA", quietly = TRUE)) { # H_infer <- sig_fit(V, W, method = "SA") # H_infer # H # # H_dt <- sig_fit(V, W, method = "SA", return_class = "data.table") # H_dt # # ## Modify arguments to method # sig_fit(V, W, method = "SA", maxit = 10, temperature = 100) # # ## Show results # show_sig_fit(H_infer) # show_sig_fit(H_dt) # }