  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.
