Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE

TL;DR

This paper introduces a CCP-assisted approach to enhance UMAP and t-SNE visualizations of scRNA-seq data, leading to more accurate and meaningful representations of cellular heterogeneity.

Contribution

The study demonstrates that integrating CCP as an initialization step significantly improves the quality and accuracy of UMAP and t-SNE visualizations for scRNA-seq data.

Findings

CCP improves visualization clarity in scRNA-seq data

Enhanced accuracy of UMAP and t-SNE with CCP initialization

Validated on eight public datasets

Abstract

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Correlated clustering and projection (CCP) was recently introduced as an effective method for preprocessing scRNA-seq data. CCP utilizes gene-gene correlations to partition the genes and, based on the partition, employs cell-cell interactions to obtain super-genes. Because CCP is a data-domain approach that does not require matrix diagonalization, it can be used in many downstream machine learning tasks. In this work, we utilize CCP as an initialization tool for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE). By using eight publicly available datasets, we have found that CCP significantly improves UMAP and t-SNE visualization and dramatically improve their accuracy.