predict_scClassifyJoint {scClassify} | R Documentation

## Testing scClassify model (joint training)

### Description

Testing scClassify model (joint training)

### Usage

    predict_scClassifyJoint(
      exprsMat_test,
      trainRes,
      cellTypes_test,
      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"),
      parallel = FALSE,
      BPPARAM = BiocParallel::SerialParam(),
      verbose = FALSE
    )

### Arguments

`exprsMat_test` | A list or a matrix indicates the expression matrices of the testing datasets
`trainRes` | A 'scClassifyTrainModel' or a 'list' indicates scClassify training model
`cellTypes_test` | A list or a vector indicates cell types of the testing 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 method to select features, 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.
`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")
    data("trainClassExample_wang")

    trainClassExampleJoint <- scClassifyTrainModelList(trainClassExample_wang,
    trainClassExample_xin)

    pred_res_joint <- predict_scClassifyJoint(exprsMat_test = exprsMat_wang_subset,
    trainRes = trainClassExampleJoint,
    cellTypes_test = wang_cellTypes,
    algorithm = "WKNN",
    features = c("limma"),
    similarity = c("pearson"),
    prob_threshold = 0.7,
    verbose = FALSE)

    table(pred_res_joint$jointRes$cellTypes, wang_cellTypes)

[Package scClassify version 1.2.0 Index]