sig_fit_bootstrap: Obtain Bootstrap Distribution of Signature Exposures of a Certain Tumor Sample

Description

This can be used to obtain the confidence of signature exposures or search the suboptimal decomposition solution.

Usage

sig_fit_bootstrap(
  catalog,
  sig,
  n = 100L,
  sig_index = NULL,
  sig_db = "legacy",
  db_type = c("", "human-exome", "human-genome"),
  show_index = TRUE,
  method = c("QP", "NNLS", "SA"),
  auto_reduce = FALSE,
  SA_not_bootstrap = FALSE,
  type = c("absolute", "relative"),
  rel_threshold = 0,
  mode = c("SBS", "DBS", "ID", "copynumber"),
  find_suboptimal = FALSE,
  suboptimal_ref_error = NULL,
  suboptimal_factor = 1.05,
  ...
)

Arguments

catalog

a named numeric vector or a numeric matrix with dimension Nx1. N is the number of component, 1 is the sample.

sig

a Signature object obtained either from sig_extract or sig_auto_extract, or just a raw signature matrix/data.frame with row representing components (motifs) and column representing signatures.

the number of bootstrap replicates.

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). Note: the signature profile for different genome builds are basically same. And specific database (e.g. 'SBS_mm10') contains less signatures than all COSMIC signatures (because some signatures are not detected from Alexandrov, Ludmil B., et al. (2020)). For all available options, check the parameter setting.

db_type

only used when sig_db is enabled. "" for keeping default, "human-exome" for transforming to exome frequency of component, and "human-genome" for transforming to whole genome frequency of component. Currently only works for 'SBS'.

show_index

if TRUE, show valid indices.

method

method to solve the minimazation problem. 'NNLS' for non-negative least square; 'QP' for quadratic programming; 'SA' for simulated annealing.

auto_reduce

if TRUE, try reducing the input reference signatures to increase the cosine similarity of reconstructed profile to observed profile.

SA_not_bootstrap

if TRUE, directly run 'SA' multiple times with original input instead of bootstrap samples.

type

'absolute' for signature exposure and 'relative' for signature relative exposure.

rel_threshold

numeric vector, a signature with relative exposure lower than (equal is included, i.e. <=) this value will be set to 0 (both absolute exposure and relative exposure). In this case, sum of signature contribution may not equal to 1.

mode

signature type for plotting, now supports 'copynumber', 'SBS', 'DBS', 'ID' and 'RS' (genome rearrangement signature).

find_suboptimal

logical, if TRUE, find suboptimal decomposition with slightly higher error than the optimal solution by method 'SA'. This is useful to explore hidden dependencies between signatures. More see reference.

suboptimal_ref_error

baseline error used for finding suboptimal solution. if it is NULL, then use 'SA' method to obtain the optimal error.

suboptimal_factor

suboptimal factor to get suboptimal error, default is 1.05, i.e., suboptimal error is 1.05 times baseline error.

...

control parameters passing to argument control in GenSA function when use method 'SA'.

Value

a list

References

Huang X, Wojtowicz D, Przytycka TM. Detecting presence of mutational signatures in cancer with confidence. Bioinformatics. 2018;34(2):330<U+2013>337. doi:10.1093/bioinformatics/btx604

Examples

Run this code

# NOT RUN {
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_bootstrap <- sig_fit_bootstrap(V[, 1], W, n = 10, type = "absolute")
  ## Typically, you have to run many times to get close to the answer
  boxplot(t(H_bootstrap$expo))
  H[, 1]

  ## Return P values
  ## In practice, run times >= 100
  ## is recommended
  report_bootstrap_p_value(H_bootstrap)
  ## For multiple samples
  ## Input a list
  report_bootstrap_p_value(list(samp1 = H_bootstrap, samp2 = H_bootstrap))

  #   ## Find suboptimal decomposition
  #   H_suboptimal <- sig_fit_bootstrap(V[, 1], W,
  #     n = 10,
  #     type = "absolute",
  #     method = "SA",
  #     find_suboptimal = TRUE
  #   )
}
# }

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