Fig. 2

Dimensionality reduction method performance evaluated by k-means clustering based on NMI in downstream cell clustering analysis. We compared 18 dimensionality reduction methods (columns), including 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). We evaluated their performance on 14 real scRNA-seq data sets (UMI-based data are labeled as purple; non-UMI-based data are labeled as blue) and 2 simulated data sets (rows). The simulated data based on Kumar data is labeled with #. The performance of each dimensionality reduction method is measured by normalized mutual information (NMI). For each data set, we compared the four different numbers of low-dimensional components. The four numbers equal to 0.5%, 1%, 2%, and 3% of the total number of cells in big data and equal to 2, 6, 14, and 20 in small data (which are labeled with*). For convenience, we only listed 0.5%, 1%, 2%, and 3% on x-axis. No results for ICA are shown in the table (gray fills) because ICA cannot handle the large number of features in that data. No results for LTSA are shown (gray fills) because error occurred when we applied the clustering method on LTSA extracted low-dimensional components there. Note that, for tSNE, we only extracted two low-dimensional components due to the limitation of the tSNE software