FeatPCA: A feature subspace based principal component analysis technique for enhancing clustering of single-cell RNA-seq data

TL;DR

FeatPCA introduces a novel subspace-based PCA method for scRNA-seq data that improves clustering accuracy by dividing data into subspaces, applying dimension reduction, and merging results, outperforming existing methods.

Contribution

The paper presents FeatPCA, a new subspace-based PCA technique that enhances clustering of high-dimensional scRNA-seq data by dividing data into subspaces before reduction.

Findings

Subspace clustering improves accuracy over full dataset methods.

FeatPCA outperforms existing clustering tools across multiple datasets.

The method offers four variations for subspacing.

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the cellular level. By providing data on gene expression for each individual cell, scRNA-seq generates large datasets with thousands of genes. However, handling such high-dimensional data poses computational challenges due to increased complexity. Dimensionality reduction becomes crucial for scRNA-seq analysis. Various dimensionality reduction algorithms, including Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-Distributed Stochastic Neighbor Embedding (t-SNE), are commonly used to address this challenge. These methods transform the original high-dimensional data into a lower-dimensional representation while preserving relevant information. In this paper we propose {\methodname}. Instead of applying dimensionality reduction directly to the entire dataset, we divide it into multiple subspaces. Within each subspace, we apply dimension reduction techniques, and then merge the reduced data. {\methodname} offers four variations for subspacing. Our experimental results demonstrate that clustering based on subspacing yields better accuracy than working with the full dataset. Across a variety of scRNA-seq datasets, {\methodname} consistently outperforms existing state-of-the-art clustering tools.