Compute fitted means from a negative binomial GLM fit

Computes the fitted mean matrix mu = exp(gmean + tcrossprod(alpha, W)) from the per-gene coefficients of a negative binomial GLM, with optional per-gene winsorisation of the log-means to median +/- winsor * MAD to bound the influence of extreme fitted values. Exposed so downstream packages (e.g. spiDE) can reconstruct fitted means from a fitNB result; for the generic (intercept-free) fit pass gmean = rep(0, nrow(alpha)).

Usage

calculateMu(gmean, alpha, W, winsor = DEFAULT_WINSOR)

Arguments

gmean

a numeric vector of per-gene intercepts (length nrow(alpha)).

alpha

a genes x covariates matrix of coefficients.

W

a cells x covariates design matrix.

winsor

a numeric, the number of MADs at which per-gene log-means are winsorised (default 4); Inf disables winsorisation.

Value

a genes x cells matrix of fitted means.

Examples

set.seed(1)
Y <- matrix(rpois(20 * 50, 5), 20, 50)
W <- cbind(1, scale(seq_len(50)))
fit <- fitNB(Y, W, verbose = FALSE)
mu <- calculateMu(fit$gmean, fit$alpha, W)
dim(mu)
#> [1] 20 50