train_scClassify {scClassify} | R Documentation

## Training scClassify model

### Description

Training scClassify model

### Usage

    train_scClassify(
      exprsMat_train,
      cellTypes_train,
      tree = "HOPACH",
      selectFeatures = "limma",
      topN = 50,
      hopach_kmax = 5,
      pSig = 0.05,
      cellType_tree = NULL,
      weightsCal = FALSE,
      parallel = FALSE,
      BPPARAM = BiocParallel::SerialParam(),
      verbose = TRUE,
      returnList = TRUE,
      ...
    )

### Arguments

`exprsMat_train` | A matrix of log-transformed expression matrix of reference dataset
`cellTypes_train` | A vector of cell types of reference dataset
`tree` | A vector indicates the method to build hierarchical tree, set as "HOPACH" by default. This should be one of "HOPACH" and "HC" (using stats::hclust).
`selectFeatures` | A vector indicates the gene selection method, set as "limma" by default. This should be one or more of "limma", "DV", "DD", "chisq", "BI".
`topN` | An integer indicates the top number of features that are selected
`hopach_kmax` | An integer between 1 and 9 specifying the maximum number of children at each node in the HOPACH tree.
`pSig` | A numeric indicates the cutoff of pvalue for features
`cellType_tree` | A list indicates the cell type tree provided by user. (By default, it is NULL)
`weightsCal` | A logical input indicates whether we need to calculate the weights for the model.
`parallel` | A logical input indicates whether the algorihms will run in parallel
`BPPARAM` | A `BiocParallelParam` class object from the `BiocParallel` package is used. Default is SerialParam().
`verbose` | A logical input indicates whether the intermediate steps will be printed
`returnList` | A logical input indicates whether the output will be class of list
`...` | Other input for predict_scClassify for the case when weights calculation of the pretrained model is performed

### Value

list of results or an object of `scClassifyTrainModel`

### Author(s)

Yingxin Lin

### Examples

    data("scClassify_example")
    xin_cellTypes <- scClassify_example$xin_cellTypes
    exprsMat_xin_subset <- scClassify_example$exprsMat_xin_subset
    trainClass <- train_scClassify(exprsMat_train = exprsMat_xin_subset,
    cellTypes_train = xin_cellTypes,
    selectFeatures = c("limma", "BI"),
    returnList = FALSE
    )

[Package scClassify version 1.4.0 Index]