MAGAN: Aligning Biological Manifolds

TL;DR

MAGAN is a novel generative adversarial network designed to align different biological measurement manifolds, improving data integration and correlation detection in multi-omics single-cell analysis.

Contribution

Introduces MAGAN, a GAN specifically designed for aligning biological manifolds, addressing limitations of previous GANs in manifold alignment tasks.

Findings

MAGAN successfully aligns single-cell genomic and proteomic data.

Improves known marker correlations compared to existing models.

Demonstrates effectiveness in biological data integration.

Abstract

It is increasingly common in many types of natural and physical systems (especially biological systems) to have different types of measurements performed on the same underlying system. In such settings, it is important to align the manifolds arising from each measurement in order to integrate such data and gain an improved picture of the system. We tackle this problem using generative adversarial networks (GANs). Recently, GANs have been utilized to try to find correspondences between sets of samples. However, these GANs are not explicitly designed for proper alignment of manifolds. We present a new GAN called the Manifold-Aligning GAN (MAGAN) that aligns two manifolds such that related points in each measurement space are aligned together. We demonstrate applications of MAGAN in single-cell biology in integrating two different measurement types together. In our demonstrated examples, cells from the same tissue are measured with both genomic (single-cell RNA-sequencing) and proteomic (mass cytometry) technologies. We show that the MAGAN successfully aligns them such that known correlations between measured markers are improved compared to other recently proposed models.