predict_scClassifyJoint {scClassify} | R Documentation |
Testing scClassify model (joint training)
predict_scClassifyJoint( 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"), 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 |
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)