Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization
This work addresses a bottleneck in scRNA-seq analysis for computational biology, offering incremental improvements to existing NMF methods.
The authors tackled the lack of multiscale analysis in nonnegative matrix factorization (NMF) for single-cell RNA sequencing data by introducing topological NMF (TNMF) and robust TNMF (rTNMF), which significantly outperformed other NMF-based methods across 12 datasets.
Single-cell RNA sequencing (scRNA-seq) is a relatively new technology that has stimulated enormous interest in statistics, data science, and computational biology due to the high dimensionality, complexity, and large scale associated with scRNA-seq data. Nonnegative matrix factorization (NMF) offers a unique approach due to its meta-gene interpretation of resulting low-dimensional components. However, NMF approaches suffer from the lack of multiscale analysis. This work introduces two persistent Laplacian regularized NMF methods, namely, topological NMF (TNMF) and robust topological NMF (rTNMF). By employing a total of 12 datasets, we demonstrate that the proposed TNMF and rTNMF significantly outperform all other NMF-based methods. We have also utilized TNMF and rTNMF for the visualization of popular Uniform Manifold Approximation and Projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE).