getDecisions {celda} | R Documentation |
Get decisions for a matrix of features. Estimate cell cluster membership using feature matrix input.
getDecisions(rules, features)
rules |
List object. The 'rules' element from 'findMarkers' output. Returns NA if cluster estimation was ambiguous. |
features |
A L(features) by N(samples) numeric matrix. |
A character vector of label predicitions.
library(M3DExampleData) counts <- M3DExampleData::Mmus_example_list$data # Subset 500 genes for fast clustering counts <- as.matrix(counts[1501:2000, ]) # Cluster genes ans samples each into 10 modules cm <- celda_CG(counts = counts, L = 10, K = 5, verbose = FALSE) # Get features matrix and cluster assignments factorized <- factorizeMatrix(counts, cm) features <- factorized$proportions$cell class <- clusters(cm)$z # Generate Decision Tree DecTree <- findMarkers(features, class, oneoffMetric = "modified F1", threshold = 1, consecutiveOneoff = FALSE) # Get sample estimates in training data getDecisions(DecTree$rules, features)