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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