Preliminary study for a fully automated pre-gating method for high-dimensional mass cytometry data

Abstract

Mass cytometry as an advanced single-cell analysis technology can produce high-dimensional data consisting of millions of cells and more than 50 features. Therefore the cell subtypes identification is difficult and impossible to be done manually. Each step of the analysis affect the results and may cause a loss of rare sub-populations of interest. One of the first steps in the analysis is pre-gating which involves filtering out unwanted measurements like debris or doublets. The existing semi-automated solutions for pre-gating require some parameters to be set which may lead to different results. Moreover, the tools often use downsampling from millions to thousands of cells. Despite the existing methods, there is still a need for a fully automated tool that will be independent of sample size. In the study, we developed a solution based on Gaussian Mixture Model (GMM) decomposition and grouping of its components into clusters. Based on the clusters we propose filtration criteria that identify measurements to be removed from the analysis. The algorithm was validated on two independent public datasets. The results are promising and reproducible, leaving intact, live cells that can be further analyzed.