Inference of time-varying genetic networks for cell differentiation

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K19916
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 62010:Life, health and medical informatics-related
• Research Institution: The University of Tokyo
• Principal Investigator: Nakajima Natsu 東京大学, 医科学研究所, 特任研究員 (60848373)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: 時変遺伝子ネットワーク / 1細胞解析 / 細胞分化 / LASSO回帰 / 動的計画法 / 並列化 / マルチプロセス

Outline of Final Research Achievements

In the cell differentiation or disease develop, since each of the cell state is different due to the interaction involving many genes, it is crucial to infer the genetic interaction for each cell type. In this study, we develop a method to infer the time-varying genetic networks for cell differentiation with pseudotime analysis from single-cell RNA-seq data. We applied this method to the Hematopoietic stem cell scRNA-seq data and performed the computational experiments. The results indicate that this method can infer differentiation time points of several cell types and as for the datasets which consist of a large number of cells, the run time can be improved through parallel computing for all computation.

Free Research Field

バイオインフォマティクス

Academic Significance and Societal Importance of the Research Achievements

本研究では, 細胞状態が動的に変化する仕組みに着目し, 複数の細胞種への分化に対応した時変遺伝子ネットワーク推定手法を開発する。1細胞の遺伝子発現データを適用することで, 細胞分化や疾患発症において, 細胞状態の遷移を引き起こす遺伝子間相互作用を解明することが期待される。