snv.cross.predict: snv.cross.predict

In mrrichowen/snv.predict: snv.predict uses true and false positives to improve snvs called from single cell RNA-seq data.

Description

This function makes a snv prediction model on one set of data and then applies it to another, typically one without the ground truth information required to build a cell type specific model.

Usage

snv.cross.predict(train_data, test_data, classifiers = NULL,
  threshold = NULL)

Arguments

train_data	Output from snv.bench. A data frame containing all derived snv classifiers with ground truths.
test_data	Output from snv.bench. A data frame containing all derived snv classifiers (where training_set=FALSE, though not essential).
classifiers	Output from snv.bench. A vector to keep low correlating classifiers only, all used if NULL.If applied to the training data, it will need to be used here.
threshold	Defaults to 0.5. Values 0 to 1 can be input to tune predictions for specificity or sensitivity.

Examples

snv.cross.predict(train_data,test_data,classifiers,threshold)

mrrichowen/snv.predict documentation built on May 14, 2019, 5:27 p.m.