Cell state transition features from single cell data

Abstract

Methods and systems for training a machine learning model are described. A processor can transform single cell data in a first space into projection data in a second space having a dimensionality lower than or equal to the first space. The processor can produce a cover having a plurality of sets of the projection data. The processor can determine a plurality of transition paths among the plurality of sets. A transition path can represent a transition from one cell state to another cell state. The processor can translate the transition paths from the second dimensional space to the first dimensional space. The processor can extract features from the transition paths in the first dimensional space. The processor can generate training data using the features, and use the training data to train a machine learning model for classifying cell state transitions.