Fig. 3: Clustering methods for scRNA-seq. | Nature Reviews Genetics

Fig. 3: Clustering methods for scRNA-seq.

From: Challenges in unsupervised clustering of single-cell RNA-seq data

Fig. 3

Representation of different clustering approaches for single-cell RNA sequencing (scRNA-seq) using the Deng data set42 of early mouse embryo development. a | True clusters, as defined by the authors, are based on the developmental stage (colours are the same as in Fig. 2). b | k-means separates cells into k = 5 groups. Because k-means assumes equal-sized clusters, the larger group of blastocysts is split from the other cell groups before the 8-cell and 16-cell stages are separated from each other. c | Complete-linkage hierarchical clustering creates a hierarchy of cells that can be cut at different levels (the result for k = 5 is indicated by the coloured bars at the bottom). Cutting farther down the tree would reveal finer substructures within the clusters. d,e | Louvain community detection29 is applied to a shared-nearest-neighbour graph connecting the cells and finds tightly connected communities in the graph (number of nearest neighbours used to construct the graph is five for part d and ten for part e). Increasing the number of neighbours when constructing the cell–cell graph indirectly decreases the resolution of graph-based clustering. Each clustering algorithm was implemented in R (igraph for parts d and e) and applied to the first two principal components (PCs) of the data.