RobustTree: An adaptive, robust PCA algorithm for embedded tree structure recovery from single-cell sequencing data.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Biological processes exhibit hierarchical structures, like cell development and tumor evolution.
• Single-cell sequencing (SCS) data is noisy and heterogeneous, complicating structure recovery.
• Robust Principal Component Analysis (RPCA) is effective for low-rank matrix recovery from corrupted data.

Purpose of the Study:

• To develop a unified framework, RobustTree, for reconstructing inherent topological structures in high-dimensional, noisy biological data.
• To extend RPCA for optimizing tree structure recovery from SCS data.
• To enable accurate reconstruction of both continuous and discrete-state topological structures.

Main Methods:

• RobustTree extends RPCA to incorporate tree structure optimization.
• The framework integrates data denoising, clustering, and tree reconstruction.
• An adaptive parameter selection scheme is employed for tree optimization.

Main Results:

• RobustTree accurately reconstructs underlying tree structures from noisy SCS data.
• The method demonstrates high accuracy and robustness on synthetic and real datasets.
• Both continuous and discrete-state topological structures were successfully recovered.

Conclusions:

• RobustTree provides a powerful and unified approach for analyzing complex biological structures in SCS data.
• The framework enhances understanding of cell development trajectories and tumor evolutionary histories.
• RobustTree offers a robust solution for noise and heterogeneity challenges in single-cell data analysis.