getDecisions: Gets cluster estimates using rules generated by...

In celda: CEllular Latent Dirichlet Allocation

Description

Get decisions for a matrix of features. Estimate cell cluster membership using feature matrix input.

Get decisions for a matrix of features. Estimate cell cluster membership using feature matrix input.

Usage

getDecisions(rules, features)

getDecisions(rules, features)

Arguments

rules	List object. The rules element from findMarkersTree output. Returns NA if cluster estimation was ambiguous.
features	A L (features) by N (samples) numeric matrix.

Value

A character vector of label predicitions.

A character vector of label predicitions.

Examples

## Not run: 
library(M3DExampleData)
counts <- M3DExampleData::Mmus_example_list$data
# Subset 500 genes for fast clustering
counts <- as.matrix(counts[seq(1501, 2000), ])
# Cluster genes and samples each into 10 modules
sce <- celda_CG(counts = counts, L = 10, K = 5, verbose = FALSE)
# Get features matrix and cluster assignments
factorized <- factorizeMatrix(sce)
features <- factorized$proportions$cell
class <- celdaClusters(sce)
# Generate Decision Tree
DecTree <- findMarkersTree(features,
  class,
  oneoffMetric = "modified F1",
  threshold = 1,
  consecutiveOneoff = FALSE)
# Get sample estimates in training data
getDecisions(DecTree$rules, features)

## End(Not run)

celda documentation built on Nov. 8, 2020, 8:24 p.m.