predict_scClassify {scClassify} | R Documentation |
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 )
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 |
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") 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)