Comparative Analysis of Dimension Reduction Methods for Cytometry by Time-of-Flight Data

While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. However, CyTOF profiles the proteomics makeup of single cells and is much more relevant for investigation of disease phenotypes than scRNA-seq. CyTOF data are also dramatically different from scRNA-seq data in many aspects. This calls for the evaluation and development of statistical and computational methods specific for analyses of CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmarked 21 two-dimensional DR methods on 110 real and 425 synthetic CyTOF samples, including 10 Imaging CyTOF samples, for accuracy, scalability, stability, and usability. In particular, we checked the concordance of DR for CyTOF data against scRNA-seq data that were generated from the same samples. Surprisingly, we found that less well-known methods like SAUCIE and scvis are the overall best performers, whereas UMAP has great downstream analysis performance and SQuaD-MDS Hybrid improves upon tSNE with better accuracy. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs (e.g. global vs local structure preservation). Based on these results, we develop a set of freely available web resources to help users select the best DR methods for their own datasets, and to aid in the development of improved DR algorithms tailored to the increasingly popular CyTOF technique.