Fig. 5

Practical guideline for choosing dimensionality reduction methods in scRNA-seq analysis. Compared dimensionality reduction methods include factor analysis (FA), principal component analysis (PCA), independent component analysis (ICA), Diffusion Map, nonnegative matrix factorization (NMF), Poisson NMF, zero-inflated factor analysis (ZIFA), zero-inflated negative binomial-based wanted variation extraction (ZINB-WaVE), probabilistic count matrix factorization (pCMF), deep count autoencoder network (DCA), scScope, generalized linear model principal component analysis (GLMPCA), multidimensional scaling (MDS), locally linear embedding (LLE), local tangent space alignment (LTSA), Isomap, uniform manifold approximation and projection (UMAP), and t-distributed stochastic neighbor embedding (tSNE). The count-based methods are colored in purple while non-count-based methods are colored in blue. Methods are ranked by their average performance across the criteria from left to right. The performance is colored and numerically coded: good performance = 2 (sky blue), intermediate performance = 1 (orange), and poor performance = 0 (gray)