Testing scClassify model

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

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)
#> Ensemble learning is disabled... 
#> Using parameters: 
#> similarity  algorithm   features 
#>  "pearson"     "WKNN"    "limma" 
#> [1] "Using dynamic correlation cutoff..."
#> [1] "Using dynamic correlation cutoff..."
#> classify_res
#>                correct   correctly unassigned           intermediate 
#>            0.704590818            0.239520958            0.000000000 
#> incorrectly unassigned         error assigned          misclassified 
#>            0.000000000            0.051896208            0.003992016 
#> weights for each base method: 
#> numeric(0)