evalLogLikMatrix: Wrapper for log likelihood of (zero-inflated) negative...

In YosefLab/LineagePulse: Differential expression analysis and model fitting for single-cell RNA-seq data

Description

This likelihood function is a wrapper computes loglikelihood of entire data set by parallelising loglikelihood computation over genes.

Usage

evalLogLikMatrix(matCounts, lsMuModel, lsDispModel, lsDropModel,
  matWeights = NULL, boolConstModelOnMMLL = FALSE)

Arguments

matCounts	(count matrix genes x cells) Observed read counts, not observed are NA.
lsMuModel	(list) Object containing description of gene-wise mean parameter models.
lsDispModel	(list) Object containing description of gene-wise dispersion parameter models.
lsDropModel	(list) Object containing description of cell-wise drop-out parameter models. Set to NULL to evaluate NB likelihood as oppose to ZINB.
matWeights	(numeric matrix cells x mixtures) [Default NULL] Assignments of cells to mixtures (for strMuModel="MM").
boolConstModelOnMMLL	(bool) [Default FALSE] Whether to evaluate constant gene-wise mean model on mixture model likelihood. This is necessary for numeric stability when testing whether mixture model fits are worse than constant fits.

Value

vecLogLik (vector length number of genes) Loglikelihood of each gene under zero-inflated negative binomial model.

Author(s)

David Sebastian Fischer

See Also

Called directly by fitZINB to track convergence of estimation iteration on entire data set.

YosefLab/LineagePulse documentation built on May 6, 2019, 2:19 p.m.