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

SummarySingle-cell sequencing data is often visualized in 2-dimensional plots, including t-SNE plots. However, it is not straightforward to extract biological knowledge, such as differentially expressed genes, from these plots. Here we introduce singleCellHaystack, a methodology that addresses this problem. singleCellHaystack uses Kullback-Leibler Divergence to find genes that are expressed in subsets of cells that are non-randomly positioned on a 2D plot. We illustrate the usage of singleCellHaystack through applications on several single-cell datasets. singleCellHaystack is implemented as an R package, and includes additional functions for clustering and visualization of genes with interesting expression patterns. Availability and implementationhttps://github.com/alexisvdb/singleCellHaystack Contactalexisvdb@infront.kyoto-u.ac.jp