Transparent Single-Cell Set Classification with Kernel Mean Embeddings

TL;DR

This paper introduces a transparent, kernel mean embedding-based method for classifying single-cell data that achieves high accuracy with interpretability and lower computational cost compared to deep learning models.

Contribution

The paper presents a novel application of Kernel Mean Embedding for transparent classification of single-cell data, matching or surpassing state-of-the-art accuracy with simpler, interpretable models.

Findings

Achieves comparable or better accuracy than gating-free methods.

Model is simpler, with fewer parameters, and easier to interpret.

Provides biological insights linking cellular heterogeneity to phenotypes.

Abstract

Modern single-cell flow and mass cytometry technologies measure the expression of several proteins of the individual cells within a blood or tissue sample. Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample's associated phenotype with machine learning models. Such a large set cardinality also limits the interpretability of machine learning models due to the difficulty in tracking how each individual cell influences the ultimate prediction. We propose using Kernel Mean Embedding to encode the cellular landscape of each profiled biological sample. Although our foremost goal is to make a more transparent model, we find that our method achieves comparable or better accuracies than the state-of-the-art gating-free methods through a simple linear classifier. As a result, our model contains few parameters but still performs similarly to deep learning models with millions of parameters. In contrast with deep learning approaches, the linearity and sub-selection step of our model makes it easy to interpret classification results. Analysis further shows that our method admits rich biological interpretability for linking cellular heterogeneity to clinical phenotype.