| 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)