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,
  ...
)

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

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
)
#> after filtering not expressed genes 
#> [1] 980 674
#> [1] "Feature Selection..."
#> [1] "Number of genes selected to construct HOPACH tree 160"
#> [1] "Constructing tree ..."
#> [1] "Training...."
#> [1] "=== selecting features by: limma ===="
#> [1] "=== selecting features by: BI ===="