Fits SAVER

Fits SAVER object

saver.fit(x, x.est, do.fast, ncores, sf, scale.sf, pred.genes, pred.cells,
  null.model, ngenes = nrow(x), ncells = ncol(x),
  gene.names = rownames(x), cell.names = colnames(x), estimates.only)

saver.fit.mean(x, ncores, sf, scale.sf, mu, ngenes = nrow(x),
  ncells = ncol(x), gene.names = rownames(x),
  cell.names = colnames(x), estimates.only)

saver.fit.null(x, ncores, sf, scale.sf, ngenes = nrow(x),
  ncells = ncol(x), gene.names = rownames(x),
  cell.names = colnames(x), estimates.only)

Arguments

x	An expression count matrix. The rows correspond to genes and the columns correspond to cells.
x.est	The log-normalized predictor matrix. The rows correspond to cells and the columns correspond to genes.
do.fast	Approximates the prediction step. Default is TRUE.
ncores	Number of cores to use. Default is 1.
sf	Normalized size factor.
scale.sf	Scale of size factor.
pred.genes	Index of genes to perform regression prediction.
pred.cells	Index of cells to perform regression prediction.
null.model	Whether to use mean gene expression as prediction.
ngenes	Number of genes.
ncells	Number of cells.
gene.names	Name of genes.
cell.names	Name of cells.
estimates.only	Only return SAVER estimates. Default is FALSE.
mu	Matrix of prior means.

Value

A list with the following components

estimate

Recovered (normalized) expression

se

Standard error of estimates

info

Information about fit

Details

The SAVER method starts by estimating the prior mean and variance for the true expression level for each gene and cell. The prior mean is obtained through predictions from a Lasso Poisson regression for each gene implemented using the glmnet package. Then, the variance is estimated through maximum likelihood assuming constant variance, Fano factor, or coefficient of variation variance structure for each gene. The posterior distribution is calculated and the posterior mean is reported as the SAVER estimate.