fitPi_SingleCell: Optim wrapper for drop-out model fitting on single cell

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 cell 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_SingleCell(vecParamGuess, vecCounts, vecMuParam, scaNormConst,
  vecDispParam, matPiAllPredictors, lsDropModelGlobal)

Arguments

vecParamGuess	(numeric vector length linear model) Parameter estimates for logit linear model for drop-out rate.
vecCounts	(count vector number of genes) Observed read counts, not observed are NA.
vecMuParam	(vector number of cells) Negative binomial mean parameter estimate.
scaNormConst	(scalar) Model scaling factors, one per cell.
vecDispParam	(vector number of cells) Negative binomial dispersion parameter estimate.
matPiAllPredictors	(matrix genes x predictors) Predictors of the drop-out rate in the linear model. Minimum are a constant offset and log of the negative binomial mean parameter. Other gene-specific predictors can be added.
lsDropModelGlobal	(list) Object containing meta-data 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_SingleCell_comp.

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