View source: R/similarity_bootstrap.R
similarity_bootstrap | R Documentation |
Bootstraps training and prediction of scRNA-seq data using binomial/multinomial logistic regression with tunable penalties
similarity_bootstrap(
trainingSet,
trainingCellType,
testingSet,
nboots = 50,
simplify = FALSE,
percent_probability = TRUE,
bs_nCores = 10,
train_nCores = parallel::detectCores(),
verbose = FALSE,
...
)
trainingSet |
Seurat object, SummarizedExperiment object or expression matrix for training |
trainingCellType |
The cell types/clusters in the training data set |
testingSet |
Seurat object, SummarizedExperiment object or expression matrix for testing |
nboots |
integet specifying number of bootstrap runs to run |
simplify |
logical. collapse the predictions to a single data frame |
percent_probability |
logical. return as percentage or logit |
bs_nCores |
integer specifying number of cores for parallelization (for bootstrap). |
train_nCores |
integer specifying number of cores for parallelization (for glmnet). |
verbose |
logical. if TRUE, print the training/prediction steps. Otherwise, minimal messages will be returned on the screen |
... |
other functions pass to glmnet and predict. see ?glmnet and ?predict.glmnet |
Generates prediction.
pred <- predScSimilarity(model, test.sce)
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