redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer

Kaikun Xie; Zehua Liu; Ning Chen; Ting Chen

Genomics, Proteomics & Bioinformatics (Apr 2021)

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer

Kaikun Xie,
Zehua Liu,
Ning Chen,
Ting Chen

Affiliations

Kaikun Xie: Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
Zehua Liu: Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China; Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
Ning Chen: Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China; Corresponding authors.
Ting Chen: Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China; Corresponding authors.

Journal volume & issue: Vol. 19, no. 2
pp. 292 – 305

Abstract

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The recent advancement of single-cell RNA sequencing (scRNA-seq) technologies facilitates the study of cell lineages in developmental processes and cancer. In this study, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets, as well as other single-cell transcriptome data. In particular, we identified a stem cell-like subpopulation in malignant glioma cells. These cells express known proliferative markers, such as GFAP, ATP1A2, IGFBPL1, and ALDOC, and remain silenced for quiescent markers such as ID3. Furthermore, we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time. redPATH is available at https://github.com/tinglabs/redPATH.

Published in Genomics, Proteomics & Bioinformatics

ISSN: 1672-0229 (Print); 2210-3244 (Online)
Publisher: Elsevier
Country of publisher: China
LCC subjects: Science: Biology (General)
Website: http://www.journals.elsevier.com/genomics-proteomics-and-bioinformatics/

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