Abstract: Techniques for identifying types of cells present in a biological sample using flow cytometry performed using a panel of markers and multiple machine learning models. The techniques include: obtaining flow cytometry data for the biological sample, the biological sample previously-obtained from a subject and comprising a plurality of cells, the flow cytometry data including flow cytometry measurements obtained during respective flow cytometry events, the flow cytometry events including a subset of events corresponding to cells in the biological sample being measured by the flow cytometry platform; and identifying types of cells of the plurality of cells using the multiple machine learning models to obtain a respective plurality of cell types.

Abstract: Techniques for determining a respective cell type for each of at least some of a plurality of cells. The techniques includes: obtaining cytometry data for a biological sample from a subject, the biological sample comprising a plurality of cells including a first cell, the cytometry data including first cytometry data for the first cell; and determining a respective type for each of at least some of the plurality of cells using a hierarchy of machine learning models corresponding to a hierarchy of cell types, the determining comprising determining a first type for the first cell by processing the first cytometry data using a first subset of the hierarchy of machine learning models.