decompressDropoutRateByGene: Compute dropout rate parameter estimates from dropout rate...

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

Description

Compute dropout rate parameter estimates from dropout rate model for a gene.

Usage

decompressDropoutRateByGene(matDropModel, vecMu = NULL,
  vecPiConstPredictors = NULL, lsDropModelGlobal)

Arguments

matDropModel	(numerical matrix cell x number of model parameters) offset parameter, log(mu) parameter, parameters belonging to constant predictors Parameters of dropout rate model for all cells.
vecMu	(numerical vector number of genes) Mean parameter estimates of all cells for given gene.
vecPiConstPredictors	(numerical vector number of constant model predictors) Other model predictors than offset and the dynamically changing mean parameter. Examples are GC- content and other gene-specific properties. This would be the global parameters as listed in the other decompression function. Here those are not a list as there is only one object.
lsDropModelGlobal	(list) Object containing meta-data of cell-wise dropout parameter models.

Value

vecPi (numerical vector number of cells) Dispersion parameter estimates for given gene (one per cell for given gene).

Author(s)

David Sebastian Fischer

See Also

Called by fitZINB.

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