mSigTools (version 1.0.7)

TP_FP_FN_avg_sim: Find best matches (by cosine similarity) of a set of mutational signatures to a set of reference mutational signatures.

Description

Find best matches (by cosine similarity) of a set of mutational signatures to a set of reference mutational signatures.

Usage

TP_FP_FN_avg_sim(extracted.sigs, reference.sigs, similarity.cutoff = 0.9)

Value

A list with the elements

• TP The number of true positive extracted signatures.

• FP The number of false positive extracted signatures.

• FN The number of false negative reference signatures.

• avg.cos.sim The average cosine similarity of true positives to their matching reference signatures.

• table A data.frame of extracted signatures that matched a reference signature. Each row contains the extracted signature name, the reference signature name, and the cosine similarity of the match.

• sim.matrix The numeric distance or similarity matrix between extracted.sigs and reference.sigs as returned from sig_dist_matrix.

• unmatched.ex.sigs The identifiers of the extracted signatures that did not match a reference signature.

• unmatched.ref.sigs The identifiers of the reference signatures that did not match an extracted signature.

Arguments

extracted.sigs

Mutational signatures discovered by some analysis. A numerical-matrix-like object with columns as signatures.

reference.sigs

A numerical-matrix-like object with columns as signatures. This matrix should contain the reference mutational signatures. For example, these might be from a synthetic data set or they could be from reference set of signatures, such as the signatures at the COSMIC mutational signatures web site. See CRAN package cosmicsig.

similarity.cutoff

A signature in reference.sigs must be matched by >= similarity.cutoff by a signature in extracted.sigs to be considered detected.

Details

Match signatures in extracted.sigs to signatures in reference.sigs using match_two_sig_sets based on cosine similarity.

Examples

ex.sigs <- matrix(c(0.2, 0.8, 0.3, 0.7, 0.6, 0.4), nrow = 2)
colnames(ex.sigs) <- c("ex1", "ex2", "ex3")
ref.sigs <- matrix(c(0.21, 0.79, 0.19, 0.81), nrow = 2)
colnames(ref.sigs) <- c("ref1", "ref2")
TP_FP_FN_avg_sim(
  extracted.sigs = ex.sigs,
  reference.sigs = ref.sigs,
  similarity.cutoff = .9
)