library("SpaNorm")
set.seed(1)
W <- cbind(1, rnorm(200))
Y <- t(replicate(3, rnbinom(200, mu = exp(1 + 0.3 * W[, 2]), size = 4)))
nbProfilePsi(Y, W, matrix(c(1, 0.3), 3, 2, byrow = TRUE), rep(0.5, 3))[1] 0.2917504 0.2388419 0.2547512Profile-ML dispersion at fixed coefficients, blocked over genes
Description
For each gene, the negative binomial dispersion that maximises the likelihood at the mean exp(W alpha + offset), the coefficients held fixed: one profile search per gene and no Newton, by the same bisection on the score that polishNB()’s batched engine uses. A gene whose optimum sits on a bound of psi.range (an under-dispersed or near-empty gene), or whose search is not finite, keeps its input dispersion rather than a boundary value stored as an estimate, the rule polishNB() applies. A caller that re-polishes the mean at a held dispersion (polishNB(warm = TRUE)) uses this to put the dispersion back at the reported mean.
Usage
nbProfilePsi(
Y,
W,
alpha,
psi,
psi.range = c(0.001, 1000),
block.size = NULL,
BPPARAM = BiocParallel::SerialParam(),
offset = NULL
)
Arguments
Y
|
a genes x cells matrix of counts (dense, sparse or DelayedArray; densified one gene block at a time). |
W
|
a cells x p numeric design matrix, or an nbBlockDesign().
|
alpha
|
a genes x p matrix of coefficients. |
psi
|
the per-gene dispersions (length nrow(Y), or one value for every gene), kept for a gene whose optimum is on a bound.
|
psi.range
|
the search interval for the dispersion. |
block.size, BPPARAM
|
gene blocking and dispatch, as in polishNB().
|
offset
|
NULL, a per-cell log-scale offset (length ncol(Y)) or a genes x cells matrix, added to every linear predictor. The dispersion is profiled at the mean it gives, so the offset must be the one the coefficients were fitted with.
|
Value
a numeric vector of dispersions, one per gene.
See Also
polishNB().