Fig. 1

Overview of the evaluation workflow for dimensionality reduction methods. We obtained a total of 30 publicly available scRNA-seq data from GEO and 10X Genomics website. We also simulated two addition simulation data sets. For each of the 32 data sets in turn, we applied 18 dimensionality reduction methods to extract the low-dimensional components. Afterwards, we evaluated the performance of dimensionality reduction methods by evaluating how effective the low-dimensional components extracted from dimensionality reduction methods are for downstream analysis. We did so by evaluating the two commonly applied downstream analysis: clustering analysis and lineage reconstruction analysis. In the analysis, we varied the number of low-dimensional components extracted from these dimensionality reduction methods. The performance of each dimensionality reduction method is qualified by Jaccard index for neighborhood preserving, normalized mutual information (NMI) and adjusted rand index (ARI) for cell clustering analysis, and Kendall correlation coefficient for trajectory inference. We also recorded the stability of each dimensionality reduction method across data splits and recorded the computation time for each dimensionality reduction method. Through the comprehensive evaluation, we eventually provide practical guidelines for practitioners to choose dimensionality reduction methods for scRNA-seq data analysis