signeR: signeR
In signeR: Empirical Bayesian approach to mutational signature discovery

Description Usage Arguments Value Examples

View source: R/signeR.R

Generates the signatures.

    signeR(M, Mheader = TRUE, samples = "rows", Opport = NA,
        Oppheader = FALSE, nsig = NA, nlim = c(NA, NA),
        try_all = FALSE, ap = NA, bp = NA, ae = NA, be = NA,
        lp = NA, le = NA, var.ap = 10, var.ae = 10,
        testing_burn = 1000, testing_eval = 1000, EM_eval = 100,
        main_burn = 10000, main_eval = 2000, start = "lee",
        estimate_hyper = FALSE, EMit_lim=100)

`M`	mutation counts matrix of samples x features.
`Mheader`	if M have colnames defined use TRUE, if FALSE a default order will be assumed.
`samples`	if the samples are row-wise or column-wise in M, default is "row".
`Opport`	context count matrix of samples x features in the target genome or region.
`Oppheader`	if Opport have header defined.
`nsig`	number of signatures, which can be provided or estimated by the algorithm.
`nlim`	define a interval to search for the optimal number of signatures.
`try_all`	if true, all possible values for nsig will be tested
`ap`	shape parameter of the gamma distribution used to generate the entries of a matrix of rate parameters of the gamma distributions which generate signatures.
`bp`	rate parameter of the gamma distribution used to generate the entries of a matrix of rate parameters of the gamma distributions which generate signatures.
`ae`	shape parameter of the gamma distribution used to generate the entries of a matrix of rate parameters of the gamma distributions which generate exposures.
`be`	rate parameter of the gamma distribution used to generate the entries of a matrix of rate parameters of the gamma distributions which generate exposures.
`lp`	parameter of the exponential distribution used to generate the entries of a matrix of shape parameters of the gamma distributions which generate signatures.
`le`	parameter of the exponential distribution used to generate the entries of a matrix of shape parameters of the gamma distributions which generate exposures.
`var.ap`	variance of the gamma distribution used to generate proposals for shape parameters of signatures
`var.ae`	variance of the gamma distribution used to generate proposals for shape parameters of exposures
`testing_burn`	number of burning iterations of the Gibbs sampler used to estimate the number of signatures in data. Corresponds to R0 at Algorithm 1 on signeR paper.
`testing_eval`	number of iterations of the Gibbs sampler used to estimate the number of signatures in data. Corresponds to R2 at Algorithm 1 on signeR paper.
`EM_eval`	number of samples generated at each iteration of the EM algorithm. Corresponds to R1 at Algorithm 1 on signeR paper.
`main_burn`	number of burning iterations of the final Gibbs sampler.
`main_eval`	number of iterations of the final Gibbs sampler.
`start`	NMF algorithm used to generate initial values for signatures and exposures,options: "brunet","KL","lee","Frobenius","offset","nsNMF", "ls-nmf","pe-nmf","siNMF","snmf/r" or "snmf/l".
`estimate_hyper`	if TRUE, algorithm estimates optimal values of ap,bp,ae,be,lp,le. Start values can still be provided.
`EMit_lim`	limit of EM iterations for the estimation of hyper-hyperparameters ap,bp,ae,be,lp,le. Default is 100. Corresponds to U at Algorithm 1 on signeR paper.

signeR output is a list with the following items:

`Nsign`	selected number of signatures.
`tested_n`	array containing the numbers of signatures tested by the algorithm.
`Test_BICs`	list of measured BIC values when testing different numbers of signatures.
`Phat`	Estimated signatures, median of P samples.
`Ehat`	Estimated exposures, median of E samples.
`SignExposures`	SignExp object which contain the set of samples for the model parameters.
`Bics`	measured BIC values on the final run of the sampler.
`HyperParam`	evolution of estimated hyperparameters when testing different numbers of signatures.