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Development of multiple classification and comparison methods of cells based on information criteria and their applications to Cell Atlas

Research Project

Project/Area Number 19K22894
Research Category

Grant-in-Aid for Challenging Research (Exploratory)

Allocation TypeMulti-year Fund
Review Section Medium-sized Section 62:Applied informatics and related fields
Research InstitutionOsaka University

Principal Investigator

MATSUDA Hideo  大阪大学, 情報科学研究科, 教授 (50183950)

Co-Investigator(Kenkyū-buntansha) 瀬尾 茂人  大阪大学, 情報科学研究科, 准教授 (30432462)
Project Period (FY) 2019-06-28 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Keywordsクラスタリング / 細胞系譜推定 / 細胞アトラス / 1細胞RNAシーケンシング解析 / バイオインフォマティクス / 細胞系譜解析 / 1細胞RNAシーケンス解析 / 遺伝子発現解析 / 1細胞RNAシーケンス / 1細胞トランスクリプトーム解析
Outline of Research at the Start

1細胞RNAシーケンス技術の発展により、様々な臓器・組織の細胞集団の持つ遺伝子発現プロファイルが細胞アトラスとしてまとめられつつある。本研究では、細胞集団中で遺伝子発現に関連が認められる細胞どうしを辺でつないだネットワークを構築し、ベイズ情報量基準に基づくスコアを導入して、スコアの高い部分ネットワークを抽出することで細胞間相互作用のネットワークを推定する。次元削減を使わずに、ネットワーク中での辺の接続関係をもとに細胞集団を比較・分類することが独創的な点である。

Outline of Final Research Achievements

Aiming to compare the similarity between cell atlas and cell populations by comparing expression profiles obtained by single-cell RNA sequencing, we developed a method for clustering cell populations by removing batch effects, a factor that hinders comparison. Expanding on this, we developed a method to infer the cell lineages of cell populations and to detect biomarker genes that are differentially expressed in the two groups. In fact, when this method was applied to the data of two types of T cells obtained from human samples, a cell lineage reflecting the differentiation process of T cells was obtained, and a new marker gene that shows characteristic expression variation in the cell population of the disease type was detected on the lineage, confirming the validity of this method. The effectiveness of this method was demonstrated by this analysis results.

Academic Significance and Societal Importance of the Research Achievements

研究者は、自身の取得した細胞サンプルの遺伝子発現プロファイルを問合せとして細胞アトラスを検索し、類似したプロファイルを持つ細胞の情報を得ることで、細胞の機能についての手がかりをつかむことが期待される、しかし、異なるサンプル間でのデータの偏りにより生じるバッチ効果のため、細胞サンプル間の比較は容易ではなかった。本研究は、複数の細胞集団で、バッチ効果を補正して相互の類似性を基にクラスタリングする手法を開発した。さらに、この手法を応用して、疾患型と健常型など生物学的背景の異なる細胞集団間で分化系譜を推定し、対応する系譜間で疾患型のみで強く発現するマーカー遺伝子を検出することができるようになった。

Report

(4 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • Research Products

    (5 results)

All 2022 2021 2020 2019

All Journal Article (2 results) (of which Peer Reviewed: 2 results,  Open Access: 2 results) Presentation (3 results) (of which Int'l Joint Research: 3 results)

  • [Journal Article] Identification of Lineage Markers for T Cell Immune Dysregulation in Sarcoidosis using Single-Cell RNA-seq2022

    • Author(s)
      Akihiro Nomura, Hideo Matsuda
    • Journal Title

      International Journal of Bioscience, Biochemistry and Bioinformatics

      Volume: 12

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] A Method for Detection of Markers for Epithelial-Mesenchymal Transition based on Single Cell Transcriptomic Data2022

    • Author(s)
      Kosho Murayama, Hideo Matsuda
    • Journal Title

      Proceedings of 12th International Conference on Bioscience, Biochemistry and Bioinformatics

      Volume: - Pages: 57-62

    • DOI

      10.1145/3510427.3510436

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Presentation] Cell trajectory inference for mouse stem cell differentiation using cell type information from single-cell RNA-seq data2021

    • Author(s)
      Yutaka Izeki, Mona Mizutome, Junko Yoshida, Shigeto Seno, Kyoji Horie, Hideo Matsuda
    • Organizer
      29th Conference on Intelligent Systems for Molecular Biology and 20th European Conference on Computational Biology (ISMB/ECCB)
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Cell trajectory inference for revealing differentiation process of mouse stem cells using single cell RNA-seq data2020

    • Author(s)
      Takumi Adachi, Junko Yoshida, Shigeto Seno, Kyoji Horie, Hideo Matsuda
    • Organizer
      28th Conference on Intelligent Systems for Molecular Biology (ISMB)
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] A study on novel estimation method of cell differentiation lineage by single cell trajectory inference2019

    • Author(s)
      Shuhei Yao, Hironori Shigeta, Shigeto Seno, Hideo Matsuda
    • Organizer
      27th Conference on Intelligent Systems for Molecular Biology and 18th European Conference on Computational Biology (ISMB/ECCB)
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research

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Published: 2019-07-04   Modified: 2023-01-30  

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