getDecisions {celda} | R Documentation

## Gets cluster estimates using rules generated by 'celda::findMarkers'

### Description

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

### Usage

    getDecisions(rules, features)

### Arguments

`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.

### Value

A character vector of label predicitions.

### Examples

    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)

[Package celda version 1.2.4 Index]