fitLPModels: Fit all models necessary for LineagePulse

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

Description

Fit alternative H1 and null H0 on both ZINB and NB noise model to a data set using cycles of coordinate ascent. The algorithm first fits the either H1 or H0 together with the dropout model by iterating over cell-wise (dropout models) and gene-wise (negative binomial models) parameters. Subsequently, the remaining model (H0 or H1) is estimated by iterating over zero-inflated negative binomial mean and dispersion parameter estimation condition on the previously estimated logistic drop-out model. The NB noise model based models are estimated in parallel across genes.

Usage

fitLPModels(objLP, matPiConstPredictors, strMuModel = "constant",
  strDispModelFull = "constant", strDispModelRed = "constant",
  strDropModel = "logistic_ofMu", strDropFitGroup = "PerCell",
  boolEstimateNoiseBasedOnH0 = TRUE, scaMaxEstimationCycles = 20,
  boolVerbose = FALSE, boolSuperVerbose = FALSE)

Arguments

objLP	(LineagePulseObject) LineagePulseObject to which null and alternative model are to be fitted.
matPiConstPredictors	(numeric matrix genes x number of constant gene-wise drop-out predictors) 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.
strMuModel	(str) "constant", "groups", "MM", "splines","impulse" [Default "impulse"] Model according to which the mean parameter is fit to each gene in the alternative model (H1).
strDispModelFull	(str) "constant", "groups", "splines" [Default "constant"] Model according to which dispersion parameter is fit to each gene in the alternative model (H1).
strDispModelRed	(str) "constant", "groups", "splines" [Default "constant"] Model according to which dispersion parameter is fit to each gene in the null model (H0).
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.
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
boolEstimateNoiseBasedOnH0	(bool) [Default FALSE] Whether to co-estimate logistic drop-out model with the constant null model or with the alternative model. The co-estimation with the noise model typically extends the run-time of this model-estimation step strongly. While the drop-out model is more accurate if estimated based on a more realistic model expression model (the alternative model), a trade-off for speed over accuracy can be taken and the dropout model can be chosen to be estimated based on the constant null expression model (set to TRUE).
scaMaxEstimationCycles	(integer) [Default 20] Maximum number of estimation cycles performed in fitZINB(). One cycle contain one estimation of of each parameter of the zero-inflated negative binomial model as coordinate ascent.
boolVerbose	(bool) Whether to follow convergence of the iterative parameter estimation with one report per cycle.
boolSuperVerbose	(bool) Whether to follow convergence of the iterative parameter estimation in high detail with local convergence flags and step-by-step loglikelihood computation.

Value

objLP (LineagePulseObject) LineagePulseObject with models with and fitting reporters added.

Author(s)

David Sebastian Fischer

See Also

Called by runLineagePulse. Calls model estimation wrappers: fitContinuousModels.

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