saver: Runs SAVER

In mohuangx/SAVER: Single-Cell RNA-Seq Gene Expression Recovery

Runs SAVER

Description

Recovers expression using the SAVER method.

Usage

saver(
  x,
  do.fast = TRUE,
  ncores = 1,
  size.factor = NULL,
  npred = NULL,
  pred.cells = NULL,
  pred.genes = NULL,
  pred.genes.only = FALSE,
  null.model = FALSE,
  mu = NULL,
  estimates.only = FALSE
)

Arguments

x	An expression count matrix. The rows correspond to genes and the columns correspond to cells. Can be sparse.
do.fast	Approximates the prediction step. Default is TRUE.
ncores	Number of cores to use. Default is 1.
size.factor	Vector of cell size normalization factors. If x is already normalized or normalization is not desired, use size.factor = 1. Default uses mean library size normalization.
npred	Number of genes for regression prediction. Selects the top npred genes in terms of mean expression for regression prediction. Default is all genes.
pred.cells	Indices of cells to perform regression prediction. Default is all cells.
pred.genes	Indices of specific genes to perform regression prediction. Overrides npred. Default is all genes.
pred.genes.only	Return expression levels of only pred.genes. Default is FALSE (returns expression levels of all genes).
null.model	Whether to use mean gene expression as prediction.
mu	Matrix of prior means.
estimates.only	Only return SAVER estimates. Default is FALSE.

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.

Value

If 'estimates.only = TRUE', then a matrix of SAVER estimates.

If 'estimates.only = FALSE', a list with the following components

estimate	Recovered (normalized) expression.
se	Standard error of estimates.
info	Information about dataset.

The info element is a list with the following components:

size.factor	Size factor used for normalization.
maxcor	Maximum absolute correlation for each gene. 2 if not calculated
lambda.max	Smallest value of lambda which gives the null model.
lambda.min	Value of lambda from which the prediction model is used
sd.cv	Difference in the number of standard deviations in deviance between the model with lowest cross-validation error and the null model
pred.time	Time taken to generate predictions.
var.time	Time taken to estimate variance.
maxcor	Maximum absolute correlation cutoff used to determine if a gene should be predicted.
lambda.coefs	Coefficients for estimating lambda with lowest cross-validation error.
total.time	Total time for SAVER estimation.

Examples

data("linnarsson")

## Not run: 
system.time(linnarsson_saver <- saver(linnarsson, ncores = 12))

## End(Not run)

# predictions for top 5 highly expressed genes
## Not run: 
saver2 <- saver(linnarsson, npred = 5)

## End(Not run)

# predictions for certain genes
## Not run: 
genes <- c("Thy1", "Mbp", "Stim2", "Psmc6", "Rps19")
genes.ind <- which(rownames(linnarsson) %in% genes)
saver3 <- saver(linnarsson, pred.genes = genes.ind)

## End(Not run)

# return only certain genes
## Not run: 
saver4 <- saver(linnarsson, pred.genes = genes.ind, pred.genes.only = TRUE)

## End(Not run)

mohuangx/SAVER documentation built on June 16, 2022, 2:42 p.m.