PILOT-GM-VAE: patient-level analysis of single-cell disease atlas with optimal transport of Gaussian mixture variational autoencoders.

Abstract

The analysis of single-cell disease atlases represents a challenge due to the presence of batch effects, low quality of disease samples, and the multiscale nature of the data, i.e. samples are described by different cell distributions. Because of these, few computational approaches are performing sample-level disease progression analysis so far. Here, we introduce Patient-Level Analysis with Optimal Transport based on Gaussian Mixture Variational Autoencoders (PILOT-GM-VAE). PILOT-GM-VAE explores the power of GM-VAE to estimate models describing complex single-cell distributions through efficient optimal transport algorithms for estimating the distance between Gaussian Mixtures. Extensive benchmarking on 12 single-cell disease atlases and competing approaches demonstrate the performance of PILOT-GM-VAE in sample-level clustering, sample-level trajectory inference, and batch correction tasks. Moreover, we performed a case study on a breast cancer disease atlas, where PILOT-GM-VAE highlighted cellular and molecular changes associated with breast cancer disease progression.