1 Introduction

Single-cell ’omics analysis enables high-resolution characterization of heterogeneous populations of cells by quantifying measurements in individual cells and thus provides a fuller, more nuanced picture into the complexity and heterogeneity between cells. However, the data also present new and significant challenges as compared to previous approaches, especially as single-cell data are much larger and sparser than data generated from bulk sequencing methods. Dimension reduction is a key step in the single-cell analysis to address the high dimension and sparsity of these data, and to enable the application of more complex, computationally expensive downstream pipelines.

Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. corral implements CA for dimensionality reduction of a single matrix of single-cell data.

See the vignette for corralm for the multi-table adaptation of CA for single-cell batch alignment/integration.

corral can be used with various types of input. When called on a matrix (or other matrix-like object), it returns a list with the SVD output, principal coordinates, and standard coordinates. When called on a SingleCellExperiment, it returns the SingleCellExperiment with the corral embeddings in the reducedDim slot named corral. To retrieve the full list output from a SingleCellExperiment input, the fullout argument can be set to TRUE.