VoPo leverages cellular heterogeneity for predictive modeling of single-cell data
Natalie Stanley, Ina A. Stelzer, Amy S. Tsai, Ramin Fallahzadeh, Edward A. Ganio, Martin Becker, Thanaphong Phongpreecha, Huda Nassar, Sajjad Ghaemi, Ivana Marić, Anthony Culos, Alan L. Chang, Maria Xenochristou, Xiaoyuan Han, Camilo Espinosa, Kristen K. Rumer, Laura S. Peterson, Franck Verdonk, Dyani Gaudillière, Eileen S. Tsai, Dorien Feyaerts, Jakob Einhaus, Kazuo Ando, Ronald J. Wong, Gerlinde Obermoser, Gary M. Shaw, David K. Stevenson, Martin S. Angst, Brice Gaudillière, Nima Aghaeepour
Abstract
High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.