Predicting Cell Populations in Single Cell Mass Cytometry Data

作者：Tamim Abdelaal, Vincent van Unen, Thomas Höllt, Frits Koning, Marcel J. T. Reinders, Ahmed Essam Mahfouz · 发表于：Cytometry Part A · 年份：2019 · DOI：10.1002/cyto.a.23738 · 被引用次数：87 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Advanced Fluorescence Microscopy Techniques

Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance...