predict_scClassifyJoint: Testing scClassify model (joint training)
In scClassify: scClassify: single-cell Hierarchical Classification

Description Usage Arguments Value Author(s) Examples

Testing scClassify model (joint training)

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
)

`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

list of results

Yingxin Lin

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)