fitPi_ManyCells: Optim wrapper for drop-out model fitting on many cells

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

Description

This function fits a logistic drop-out model to a set of cells based on given gene-specific predictors (which enter the linear model). Parameter estimation of the linear model is performed by maximum likelihood based on the overall likelihood.

Usage

fitPi_ManyCells(vecParamGuess, matCounts, lsMuModel, lsDispModel, lsDropModel)

Arguments

vecParamGuess	(numeric vector length linear model) Initial parameter estimates for logit linear model for drop-out rate.
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.

Value

vecLinModel (numeric vector length linear model) Linear model for drop-out rate in logit space for given cell.

Author(s)

David Sebastian Fischer

See Also

Called by drop-out estimation wrapper code in fitPi. Calls fitting cost function: evalLogLikPiZINB_ManyCells_comp.

LineagePulse documentation built on Nov. 8, 2020, 7:01 p.m.