Identification of differentially expressed genes (DEGs) is a key step in single-cell
transcriptomics data analysis. 'singleCellHaystack' predicts DEGs without relying on
clustering of cells into arbitrary clusters. Single-cell RNA-seq (scRNA-seq) data is
often processed to fewer dimensions using Principal Component Analysis (PCA) and
represented in 2-dimensional plots (e.g. t-SNE or UMAP plots). 'singleCellHaystack'
uses Kullback-Leibler divergence to find genes that are expressed in subsets of cells
that are non-randomly positioned in a these multi-dimensional spaces or 2D representations.
For the theoretical background of 'singleCellHaystack' we refer to
Vandenbon and Diez (2019)