Confident Clustering via PCA Compression Ratio and Its Application to Single-cell RNA-seq Analysis

TL;DR

This paper introduces a confident clustering method that improves accuracy and stability in single-cell RNA-seq data analysis by reducing the influence of boundary data points through a PCA compression ratio approach.

Contribution

The paper presents a novel confident clustering algorithm that enhances clustering accuracy and stability, especially for biological data with boundary points, using PCA compression ratio.

Findings

High accuracy on tested single-cell RNA-seq datasets

Stable results across different parameter choices

Effective reduction of boundary data point influence

Abstract

Unsupervised clustering algorithms for vectors has been widely used in the area of machine learning. Many applications, including the biological data we studied in this paper, contain some boundary datapoints which show combination properties of two underlying clusters and could lower the performance of the traditional clustering algorithms. We develop a confident clustering method aiming to diminish the influence of these datapoints and improve the clustering results. Concretely, for a list of datapoints, we give two clustering results. The first-round clustering attempts to classify only pure vectors with high confidence. Based on it, we classify more vectors with less confidence in the second round. We validate our algorithm on single-cell RNA-seq data, which is a powerful and widely used tool in biology area. Our confident clustering shows a high accuracy on our tested datasets. In addition, unlike traditional clustering methods in single-cell analysis, the confident clustering shows high stability under different choices of parameters.