Discovering a sparse set of pairwise discriminating features in high dimensional data

TL;DR

This paper introduces an unsupervised method to identify a sparse set of features that discriminate between clusters in high-dimensional data, enabling better understanding and classification of complex biological systems.

Contribution

The authors propose a novel ensemble-based approach to discover low-dimensional discriminative features in high-dimensional data without supervision.

Findings

Identified 27 key transcription factors in mouse gastrulation data.

Revealed clear cell type signatures in a low-dimensional subspace.

Outperformed prior methods in feature selection for clustering.

Abstract

Extracting an understanding of the underlying system from high dimensional data is a growing problem in science. Discovering informative and meaningful features is crucial for clustering, classification, and low dimensional data embedding. Here we propose to construct features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low dimensional space. We propose an unsupervised method to identify the subset of features that define a low dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low dimensional subspace.