evalLogLikPiZINB_SingleCell: Cost function zero-inflated negative binomial model for...

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

Description

Log likelihood of zero inflated negative binomial model. This function is designed to allow numerical optimisation of logistic drop-out paramater model on single gene given the negative binomial mean and dispersion parameters.

Usage

evalLogLikPiZINB_SingleCell(vecTheta, vecCounts, vecMu, scaNormConst, vecDisp,
  matPiAllPredictors, strDropModel, vecidxNotZero, vecidxZero)

Arguments

vecTheta	(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.
vecMu	(vector number of cells) Negative binomial mean parameter estimate.
scaNormConst	(scalar) Model scaling factors, one per cell.
vecDisp	(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.
strDropModel	(str) "logistic_ofMu", "logistic" [Default "logistic_ofMu"] Definition of drop-out model. "logistic_ofMu" - include the fitted mean in the linear model of the drop-out rate and use offset and matPiConstPredictors. "logistic" - only use offset and matPiConstPredictors.
vecidxNotZero	(bool vector number of cells) Whether observation is larger than zero.
vecidxZero	(bool vector number of cells) Whether observation is zero.

Value

scaLogLik (scalar) Value of cost function: zero-inflated negative binomial likelihood.

Author(s)

David Sebastian Fischer

See Also

Called by fitting wrapper: fitPi_SingleCell. Calls evalLogLikZINB. Compiled version: evalLogLikPiZINB_SingleCell_comp.

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