Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data

TL;DR

This paper introduces ActiveSVM, an active learning method that identifies minimal, highly-informative gene sets for classifying cell types and disease states in single-cell mRNA-seq data, reducing costs and enabling clinical applications.

Contribution

The paper presents a novel active feature selection approach, ActiveSVM, that efficiently finds small gene sets for accurate cell classification and biological insights in large single-cell datasets.

Findings

Achieves ~90% cell-type classification accuracy

Scales to datasets with over a million cells

Generalizes to genetic perturbation and spatial transcriptomics

Abstract

Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here, we introduce an active learning method (ActiveSVM) that identifies minimal but highly-informative gene sets that enable the identification of cell-types, physiological states, and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data through an iterative cell-type classification task where misclassified cells are examined at each round of analysis to identify maximally informative genes through an `active' support vector machine (ActiveSVM) classifier. By focusing computational resources on misclassified cells, ActiveSVM scales to analyze data sets with over a million single cells. We demonstrate that ActiveSVM feature selection identifies gene sets that enable ~90% cell-type classification accuracy across a variety of data sets including cell atlas and disease characterization data sets. The method generalizes to reveal genes that respond to genetic perturbations and to identify region specific gene expression patterns in spatial transcriptomics data. The discovery of small but highly informative gene sets should enable substantial reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery, and genetic screens.