Skip to main content
Fig. 3 | Genome Biology

Fig. 3

From: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

Fig. 3

Dimensionality reduction method performance evaluated by Kendall correlation in the downstream trajectory inference analysis. We compared 17 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), 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 (rows) in terms of lineage inference accuracy. We used Slingshot with k-means as the initial step for lineage inference. The performance of each dimensionality reduction method is measured by Kendall correlation. For each data set, we compared four different numbers of low-dimensional components (2, 6, 14, and 20; four sub-columns under each column). Gray fills in the table represents missing results where Slingshot gave out errors when we supplied the extracted low-dimensional components from the corresponding dimensionality reduction method. Note that, for tSNE, we only extracted two low-dimensional components due to the limitation of the tSNE software

Back to article page