initDropModel: Initialise drop-out model container object

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

Description

Either use supplied fits from previous fitting or initialise from count data.

Usage

initDropModel(matCounts, matPiConstPredictors, lsDropModel, strDropModel,
  strDropFitGroup, MAXIT_BFGS_Pi, RELTOL_BFGS_Pi)

Arguments

matCounts	(matrix genes x cells) Count data of all cells, unobserved entries are NA.
matPiConstPredictors	(numeric matrix genes x number of constant gene-wise drop-out predictors) [Default NULL] Predictors for logistic drop-out fit other than offset and mean parameter (i.e. parameters which are constant for all observations in a gene and externally supplied.) Is null if no constant predictors are supplied.
lsDropModel	(list) [Default NULL]
strDropModel	(str) "logistic_ofMu", "logistic", "none" [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. "none" - negative binomial noise model without zero-inflation.
strDropFitGroup	(str) "PerCell", "AllCells" [Defaul "PerCell"] Definition of groups on cells on which separate drop-out model parameterisations are fit. "PerCell" - one parametersiation (fit) per cell "ForAllCells" - one parametersiation (fit) for all cells
MAXIT_BFGS_Pi	(sca) Maximum number of iterations in BFGS estimation of dropout models. This is a control parameter to optim().
RELTOL_BFGS_Pi	(sca) Relative tolerance of BFGS estimation of dropout models. This is a control parameter to optim().

Value

lsDropModel (list) Initialisation of drop-out model object.

Author(s)

David Sebastian Fischer

See Also

Called by fitModel.

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