scClassify {scClassify} | R Documentation

## Train and test scClassify model

### Description

Train and test scClassify model

### Usage

    scClassify(
      exprsMat_train = NULL,
      cellTypes_train = NULL,
      exprsMat_test = NULL,
      cellTypes_test = NULL,
      tree = "HOPACH",
      algorithm = "WKNN",
      selectFeatures = "limma",
      similarity = "pearson",
      cutoff_method = c("dynamic", "static"),
      weighted_ensemble = FALSE,
      weights = NULL,
      weighted_jointClassification = TRUE,
      cellType_tree = NULL,
      k = 10,
      topN = 50,
      hopach_kmax = 5,
      pSig = 0.01,
      prob_threshold = 0.7,
      cor_threshold_static = 0.5,
      cor_threshold_high = 0.7,
      returnList = TRUE,
      parallel = FALSE,
      BPPARAM = BiocParallel::SerialParam(),
      verbose = FALSE
    )

### Arguments

`exprsMat_train` | A matrix of log-transformed expression matrix of reference dataset
`cellTypes_train` | A vector of cell types of reference dataset
`exprsMat_test` | A list or a matrix indicates the expression matrices of the query datasets
`cellTypes_test` | A list or a vector indicates cell types of the query datasets (Optional).
`tree` | A vector indicates the method to build hierarchical tree, set as "HOPACH" by default. This should be one of "HOPACH" and "HC" (using hclust).
`algorithm` | A vector indicates the KNN method that are used, set as "WKNN" by default. Thisshould be one or more of "WKNN", "KNN", "DWKNN".
`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".
`similarity` | A vector indicates the similarity measure that are used, set as "pearson" by default. This should be one or more of "pearson", "spearman", "cosine", "jaccard", kendall", "binomial", "weighted_rank","manhattan"
`cutoff_method` | A vector indicates the method to cutoff the correlation distribution. Set as "dynamic" by default.
`weighted_ensemble` | A logical input indicates in ensemble learning, whether the results is combined by a weighted score for each base classifier.
`weights` | A vector indicates the weights for ensemble
`weighted_jointClassification` | A logical input indicates in joint classification using multiple training datasets, whether the results is combined by a weighted score for each training model.
`cellType_tree` | A list indicates the cell type tree provided by user. (By default, it is NULL) (Only for one training data input)
`k` | An integer indicates the number of neighbour
`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
`prob_threshold` | A numeric indicates the probability threshold for KNN/WKNN/DWKNN.
`cor_threshold_static` | A numeric indicates the static correlation threshold.
`cor_threshold_high` | A numeric indicates the highest correlation threshold
`returnList` | A logical input indicates whether the output will be class of list
`parallel` | A logical input indicates whether running in paralllel or not
`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

### Value

A list of the results, including testRes storing the results of the testing information, and trainRes storing the training model inforamtion.

### Author(s)

Yingxin Lin

### Examples

    data("scClassify_example")
    xin_cellTypes <- scClassify_example$xin_cellTypes
    exprsMat_xin_subset <- scClassify_example$exprsMat_xin_subset
    wang_cellTypes <- scClassify_example$wang_cellTypes
    exprsMat_wang_subset <- scClassify_example$exprsMat_wang_subset

    scClassify_res <- scClassify(exprsMat_train = exprsMat_xin_subset,
    cellTypes_train = xin_cellTypes,
    exprsMat_test = list(wang = exprsMat_wang_subset),
    cellTypes_test = list(wang = wang_cellTypes),
    tree = "HOPACH",
    algorithm = "WKNN",
    selectFeatures = c("limma"),
    similarity = c("pearson"),
    returnList = FALSE,
    verbose = FALSE)

[Package scClassify version 1.2.0 Index]