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
)

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

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)
#>               wang_cellTypes
#>                acinar alpha beta delta ductal gamma stellate
#>   acinar            4     0    0     0      0     0        0
#>   alpha             0   206    0     0      0     0        0
#>   beta              0     0  118     0      0     0        0
#>   delta             0     0    0    10      0     0        0
#>   ductal            0     0    0     0     93     0        0
#>   gamma             0     0    0     0      0    21        0
#>   intermediate      1     0    0     0      3     0        0
#>   stellate          0     0    0     0      0     0       45