Prediction of cell population size, fraction, and ratios by machine learning methods on flow cytometry data

A computer-implemented method for analyzing flow cytometry data using machine learning comprising:

• obtaining the flow cytometry data generated by a cytometry instrument representing cells of multiple categories;
• generating a feature vector representation based on the flow cytometry data using a plurality of self-organizing maps (SOMs), wherein each SOM corresponds to a different category of the multiple categories;
• and
• predicting, using a machine learning model, each of one or more target labels of the cells by applying each of one or more regression models to the feature vector representation.

A computer-implemented method for training a machine learning model, comprising:

• obtaining flow cytometry data generated by a cytometry instrument representing cells of multiple categories using a data ingestion component;
• generating a feature vector representation based on the flow cytometry data using a plurality of self-organizing maps (SOMs), wherein each SOM corresponds to a different category of the cells;
• predicting a target label of one or more target labels of the cells by applying a regression model of one or more regression models to the feature vector representation;
• computing a prediction loss based on difference between the predicted target label and ground truth data from a training dataset;
• and
• updating parameters of the machine learning model based on the prediction loss.