Uses cv.glmnet from the glmnet package to return the SAVER
prediction.
expr.predict(x, y, pred.cells = 1:length(y), seed = NULL, lambda.max = NULL, lambda.min = NULL)
| x | A log-normalized expression count matrix of genes to be used in the prediction. |
|---|---|
| y | A normalized expression count vector of the gene to be predicted. |
| pred.cells | Index of cells to use for prediction. Default is to use all cells. |
| seed | Sets the seed for reproducible results. |
| lambda.max | Maximum value of lambda which gives null model. |
| lambda.min | Value of lambda from which the prediction model is used |
A vector of predicted gene expression.
The SAVER method starts with predicting the prior mean for each cell for a
specific gene. The prediction is performed using the observed normalized
gene count as the response and the normalized gene counts of other genes as
predictors. cv.glmnet from the glmnet package is used to fit
the LASSO Poisson regression. The model with the lowest cross-validation
error is chosen and the fitted response values are returned and used as the
SAVER prediction.