Description Usage Arguments Details Value
Calculates SAVER estimate
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  calc.estimate(
x,
x.est,
cutoff = 0,
coefs = NULL,
sf,
scale.sf,
pred.gene.names,
pred.cells,
null.model,
nworkers,
calc.maxcor,
estimates.only
)
calc.estimate.mean(x, sf, scale.sf, mu, nworkers, estimates.only)
calc.estimate.null(x, sf, scale.sf, nworkers, estimates.only)

x 
An expression count matrix. The rows correspond to genes and the columns correspond to cells. 
x.est 
The lognormalized predictor matrix. The rows correspond to cells and the columns correspond to genes. 
cutoff 
Maximum absolute correlation to determine whether a gene should be predicted. 
coefs 
Coefficients of a linear fit of logsquared ratio of largest lambda to lambda of lowest crossvalidation error. Used to estimate model with lowest crossvalidation error. 
sf 
Normalized size factor. 
scale.sf 
Scale of size factor. 
pred.gene.names 
Names 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. 
nworkers 
Number of cores registered to parallel backend. 
calc.maxcor 
Whether to calculate maximum absolute correlation. 
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

Recovered (normalized) expression 

Standard error of estimates 

Maximum absolute correlation for each gene. 2 if not calculated 

Smallest value of lambda which gives the null model. 

Value of lambda from which the prediction model is used 

Difference in the number of standard deviations in deviance between the model with lowest crossvalidation error and the null model 

Time taken to generate predictions. 

Time taken to estimate variance. 
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