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

Robust Principal Component Analysis (RPCA) offers a powerful tool for recovering a low-rank matrix from highly corrupted data, with growing applications in computational biology. Biological processes commonly form intrinsic hierarchical structures, such as tree structures of cell development traject...

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Vydáno v:Frontiers in genetics Ročník 14; s. 1110899
Hlavní autoři: Chen, Ziwei, Zhang, Bingwei, Gong, Fuzhou, Wan, Lin, Ma, Liang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 08.03.2023
Témata:
clustering
data denoising
Genetics
robust principal component analysis
single-cell sequencing
tree structure reconstruction
single-cell sequencing
clustering
robust principal component analysis
data denoising
tree structure reconstruction
ISSN:1664-8021, 1664-8021
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Shrnutí:Robust Principal Component Analysis (RPCA) offers a powerful tool for recovering a low-rank matrix from highly corrupted data, with growing applications in computational biology. Biological processes commonly form intrinsic hierarchical structures, such as tree structures of cell development trajectories and tumor evolutionary history. The rapid development of single-cell sequencing (SCS) technology calls for the recovery of embedded tree structures from noisy and heterogeneous SCS data. In this study, we propose RobustTree, a unified framework to reconstruct the inherent topological structure underlying high-dimensional data with noise. By extending RPCA to handle tree structure optimization, RobustTree leverages data denoising, clustering, and tree structure reconstruction. It solves the tree optimization problem with an adaptive parameter selection scheme that we proposed. In addition to recovering real datasets, RobustTree can reconstruct continuous topological structure and discrete-state topological structure of underlying SCS data. We apply RobustTree on multiple synthetic and real datasets and demonstrate its high accuracy and robustness when analyzing high-noise SCS data with embedded complex structures. The code is available at https://github.com/ucasdp/RobustTree .
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This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics
These authors have contributed equally to this work and share first authorship
Reviewed by: Nayang Shan, Capital University of Economics and Business, China
Edited by: Lin Hou, Tsinghua University, China
Jianyong Sun, Xi’an Jiaotong University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2023.1110899