predict_scClassify {scClassify} | R Documentation

## Testing scClassify model

### Description

Testing scClassify model

### Usage

    predict_scClassify(
      exprsMat_test,
      trainRes,
      cellTypes_test = NULL,
      k = 10,
      prob_threshold = 0.7,
      cor_threshold_static = 0.5,
      cor_threshold_high = 0.7,
      features = "limma",
      algorithm = "WKNN",
      similarity = "pearson",
      cutoff_method = c("dynamic", "static"),
      weighted_ensemble = FALSE,
      weights = NULL,
      parallel = FALSE,
      BPPARAM = BiocParallel::SerialParam(),
      verbose = FALSE
    )

### Arguments

`exprsMat_test` | A list or a matrix indicates the log-transformed expression matrices of the query datasets
`trainRes` | A 'scClassifyTrainModel' or a 'list' indicates scClassify trained model
`cellTypes_test` | A list or a vector indicates cell types of the qurey datasets (Optional).
`k` | An integer indicates the number of neighbour
`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
`features` | A vector indicates the gene selection method, set as "limma" by default. This should be one or more of "limma", "DV", "DD", "chisq", "BI".
`algorithm` | A vector indicates the KNN method that are used, set as "WKNN" by default. This should be one or more of "WKNN", "KNN", "DWKNN".
`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
`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

list of results

### Author(s)

Yingxin Lin

### Examples

    data("scClassify_example")
    wang_cellTypes <- scClassify_example$wang_cellTypes
    exprsMat_wang_subset <- scClassify_example$exprsMat_wang_subset
    data("trainClassExample_xin")

    pred_res <- predict_scClassify(exprsMat_test = exprsMat_wang_subset,
    trainRes = trainClassExample_xin,
    cellTypes_test = wang_cellTypes,
    algorithm = "WKNN",
    features = c("limma"),
    similarity = c("pearson"),
    prob_threshold = 0.7,
    verbose = TRUE)

[Package scClassify version 1.4.0 Index]