Description Usage Arguments Details Value Author(s) Examples

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

1 2 3 | ```
LinearEmbeddingMatrix(sampleFactors = matrix(nrow = 0, ncol = 0),
featureLoadings = matrix(nrow = 0, ncol = 0), factorData = NULL,
metadata = list())
``` |

`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. |

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.

A LinearEmbeddingMatrix object is returned from the constructor.

Aaron Lun, Davide Risso and Keegan Korthauer

1 2 3 |

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