train_scClassify: Training scClassify model
In SydneyBioX/scClassify: scClassify: single-cell Hierarchical Classification

Description Usage Arguments Value Author(s) Examples

Training scClassify model

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,
  ...
)

`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", "Cepo".
`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

list of results or an object of scClassifyTrainModel

Yingxin Lin

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
)