corral: Single-cell RNA-seq dimension reduction, batch integration, and visualization with correspondence analysis

Abstract

AbstractEffective dimension reduction is an essential step in analysis of single cell RNA-seq (scRNAseq) count data, which are high-dimensional, sparse, and noisy. Principal component analysis (PCA) is widely used in analytical pipelines, and since PCA requires continuous data, it is often coupled with log-transformation in scRNAseq applications. However, log-transformation of scRNAseq counts distorts the data, and can obscure meaningful variation. We describe correspondence analysis (CA) for dimension reduction of scRNAseq data, which is a performant alternative to PCA. Designed for use with counts, CA is based on decomposition of a chi-squared residual matrix and does not require log-transformation of scRNAseq counts. We extend beyond standard CA (decomposition of Pearson residuals computed on the contingency table) and propose variations of CA, including an alternative chi-squared statistic, that address overdispersion and high sparsity in scRNAseq data. The performance of five variations of CA and standard CA are benchmarked on 10 datasets and compared to glmPCA. CA variations are fast, scalable, and outperform standard CA and glmPCA, to compute embeddings with more performant or comparable clustering accuracy in 8 out of 9 datasets. Of the variations we considered, we found that CA using the Freeman-Tukey chi-squared residual was most performant overall in scRNAseq data. Our analyses also showed that variance stabilizing transformations applied in conjunction with standard CA (using Pearson residuals) and the use of “power deflation” smoothing both improve performance in downstream clustering tasks, as compared to standard CA alone. CA has advantages including visual illustration of associations between genes and cell populations in a “CA biplot,” and easy extension to multi-table analysis enabling integrative dimension reduction. We introduce corralm, a CA-based method for multi-table batch integration of scRNAseq data in shared latent space, and we propose a new approach for assessing batch integration. We implement CA for scRNAseq in the corral R/Bioconductor package (https://www.bioconductor.org/packages/corral) interfaces directly with widely used single cell classes in Bioconductor, allowing for easy integration into scRNAseq pipelines.