Description Usage Arguments Details Value

Uses `cv.glmnet`

from the `glmnet`

package to return the SAVER
prediction.

1 2 3 4 5 6 7 8 | ```
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 |

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.

A vector of predicted gene expression.

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