singleCellHaystack: A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data

Summary A common analysis of single-cell sequencing data includes dimensionality reduction using t-SNE or UMAP, clustering of cells, and identifying differentially expressed genes. How cell clusters are defined has important consequences in the interpretation of results and downstream analyses, but is often not straightforward. To address this difficulty, we present a new approach called singleCellHaystack that enables the identification of differentially expressed genes (DEGs) without relying on explicit clustering of cells. Our method uses Kullback-Leibler Divergence to find genes that are expressed in subsets of cells that are non-randomly positioned in a multi-dimensional space. We illustrate the usage of singleCellHaystack through applications on several single-cell datasets, demonstrate that it enables the identification of markers important for cell subset separation in an unbiased way, and compare its results with those of a traditional, clustering-based DEG prediction method.

Availability and implementation singleCellHaystack is implemented as an R package and is available from https://github.com/alexisvdb/singleCellHaystack

Contact alexisvdb{at}infront.kyoto-u.ac.jp