saver_fit: Fits SAVER
In SAVER: Single-Cell RNA-Seq Gene Expression Recovery

Description Usage Arguments Details Value

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
)

`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.

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.

A list with the following components