LinearEmbeddingMatrix {SingleCellExperiment} | R Documentation

## LinearEmbeddingMatrix class

### Description

A description of the LinearEmbeddingMatrix class for storing low-dimensional embeddings from linear dimensionality reduction methods.

### Usage

    LinearEmbeddingMatrix(sampleFactors = matrix(nrow = 0, ncol = 0),
        featureLoadings = matrix(nrow = 0, ncol = 0), factorData = NULL,
        metadata = list())

### Arguments

`sampleFactors` | A matrix-like object of sample embeddings, where rows are samples and columns are factors.
`featureLoadings` | A matrix-like object of feature loadings, where rows are features and columns are factors.
`factorData` | A DataFrame containing factor-level information, with one row per factor.
`metadata` | An optional list of arbitrary content describing the overall experiment.

### Details

The LinearEmbeddingMatrix class is a matrix-like object that supports `dim`, `dimnames` and `as.matrix`. It is designed for the storage of results from linear dimensionality reduction methods like principal components analysis (PCA), factor analysis and non-negative matrix factorization.

The `sampleFactors` slot is intended to store The low-dimensional representation of the samples, such as the principal coordinates from PCA. The feature loadings contributing to each factor are stored in `featureLoadings`, and should have the same number of columns as `sampleFactors`. The `factorData` stores additional factor-level information, such as the percentage of variance explained by each factor, and should have the same number of rows as `sampleFactors`.

The intended use of this class is to allow PCA and other results to be stored in the `reducedDims` slot of a SingleCellExperiment object. This means that feature loadings remain attached to the embedding, allowing it to be used in downstream analyses.

### Value

A LinearEmbeddingMatrix object is returned from the constructor.

### Author(s)

Aaron Lun, Davide Risso and Keegan Korthauer

### Examples

    lem <- LinearEmbeddingMatrix(matrix(rnorm(1000), ncol=5),
        matrix(runif(20000), ncol=5))
    lem

[Package SingleCellExperiment version 1.6.0 Index]