Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding.

High-throughput single-cell gene expression profiling has enabled the definition of new cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and a popular method is t-stochastic neighbor embedding (t-SNE), which visualizes local patterns well but distorts global structure, such as distances between clusters. We developed similarity weighted nonnegative embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states on the visualization alongside the cells and maintains fidelity when visualizing local and global structure for both developmental trajectories and discrete cell types. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors; embeds the cells, genes, and factors in a 2D visualization; and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single-cell RNA-seq data from hematopoietic progenitors and human brain cells. SWNE is available as an R package at github.com/yanwu2014/swne.