Realtime normalization of flow cytometry data

A computer-implemented method for normalizing flow cytometry data comprising:

• obtaining a sequence of cytometry data output by a cytometry instrument;
• normalizing, using a real-time data normalization component, a first data point in the sequence of cytometry data using a set of normalization parameters determined based on a queue of cases belonging to a predetermined class;
• updating the queue of cases belonging to the predetermined class with the normalized first data point in response to determining, using a machine learning model, the normalized first data point belongs to the predetermined class;
• and
• normalizing, using the real-time data normalization component, a second data point in the sequence of cytometry data using an updated set of normalization parameters determined based on the updated queue of cases belonging to the predetermined class.

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

• obtaining a sequence of normalized cytometry data comprising a plurality of tubes of data;
• generating a plurality of latent representations by generating a latent representation for each tube of data based on a normalized data point in the sequence of normalized cytometry data, wherein each of the plurality of latent representations is generated using a different Self-Organizing Map (SOM) model;
• generating a concatenated representation by concatenating the plurality of latent representations;
• generating, using the machine learning model, a prediction indicating whether the normalized data point belongs to a predetermined class by applying the machine learning model to the concatenated representation;
• calculating a prediction loss based on a difference between the prediction and ground-truth data;
• and
• updating parameters of the machine learning model based on the prediction loss.