ABSTRACT
Dimensionality reduction (DR) is an indispensable analytic component for many areas of single cell RNA sequencing (scRNAseq) data analysis. Proper DR can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of DR in scRNAseq analysis and the vast number of DR methods developed for scRNAseq studies, however, few comprehensive comparison studies have been performed thus far to evaluate the effectiveness of different DR methods in scRNAseq. Here, we aim to fill this critical knowledge gap by providing a comprehensive comparative evaluation of a variety of commonly used DR methods for scRNAseq studies. Specifically, we compared 11 different DR methods on 28 publicly available scRNAseq data sets that cover a range of sequencing techniques and sample sizes. We evaluate the performance of different DR methods both for cell clustering and for lineage reconstruction in terms of their accuracy and robustness. We evaluate the computational scalability of different DR methods by recording their computational cost. Based on the comprehensive evaluation results, we provide important guidelines for choosing DR methods in scRNAseq data analysis. We also provide all analysis scripted used in the present study at www.xzlab.org/reproduce.html. Together, we hope that our results will serve as an important practical reference for practitioners to choose DR methods in the field of scRNAseq analysis.