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Comprehensive optimization of cell type-specific gene co-expression networks and construction of a cell type-specific co-expression database

Research Project

Project/Area Number 20K06609
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 43060:System genome science-related
Research InstitutionKyoto University

Principal Investigator

Vandenbon Alexis  京都大学, 医生物学研究所, 准教授 (60570140)

Project Period (FY) 2020-04-01 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2022: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2020: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Keywordsbioinformatics / gene expression / gene co-expression / data normalization / batch effect correction / database / batch effects / RNA-seq / network analysis
Outline of Research at the Start

Understanding gene regulation is one of the key questions in biology. The computational prediction of regulatory interactions is an attractive approach, but accuracy is low, even in simple eukaryotes. In this project, we will conduct a comprehensive evaluation of gene expression data normalization, batch effect correction, correlation measures, and downstream network processing steps and their effect on the quality of co-expression networks, in many human and mouse cell types. Results will be made public in a database. This project will lead to better predictions of gene regulatory mechanisms.

Outline of Final Research Achievements

We used a large collection of RNA-seq data samples covering 68 human and 76 mouse cell types and tissues to conduct a comprehensive evaluation of which data processing workflow results in the highest quality gene co-expression networks. Our results indicate that it is important to collect as many RNA-seq samples as possible. Second, researchers should use using Upper Quartile normalization and correct batch effects. Finally, in general Pearson’s correlation should be used, but in small datasets Spearman’s rank correlation might be preferable. We confirmed that using the optimized processing workflow, we obtained a high-quality gene expression dataset which can be used as a reference. We provided two illustrations of the use of our dataset as a reference to support other bioinformatics analyses. Finally, we are preparing a freely accessible gene co-expression database, which will allow users to inspect gene expression and co-expression in many human and mouse tissues and cell types.

Academic Significance and Societal Importance of the Research Achievements

Gene co-expression is widely used for the prediction of gene functions and regulatory mechanisms. We here showed how gene expression data can be processed to obtain high-quality co-expression values. This will contribute to improved bioinformatics analyses and new insights into gene regulation.

Report

(4 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Research-status Report
  • 2020 Research-status Report
  • Research Products

    (5 results)

All 2023 2022 2021

All Journal Article (4 results) (of which Peer Reviewed: 2 results,  Open Access: 4 results) Presentation (1 results)

  • [Journal Article] Murine breast cancers disorganize the liver transcriptome in a zonated manner2023

    • Author(s)
      Alexis Vandenbon、Rin Mizuno、Riyo Konishi、Masaya Onishi、Kyoko Masuda、Yuka Kobayashi、Hiroshi Kawamoto、Ayako Suzuki、Chenfeng He、Yuki Nakamura、Kosuke Kawaguchi、Masakazu Toi、Masahito Shimizu、Yasuhito Tanaka、Yutaka Suzuki、Shinpei Kawaoka
    • Journal Title

      Communications Biology

      Volume: 6(1) Issue: 1 Pages: 97-97

    • DOI

      10.1038/s42003-023-04479-w

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data2022

    • Author(s)
      Vandenbon Alexis
    • Journal Title

      PLOS ONE

      Volume: 17 Issue: 1 Pages: e0263344-e0263344

    • DOI

      10.1371/journal.pone.0263344

    • Related Report
      2022 Annual Research Report 2021 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] A universal differential expression prediction tool for single-cell and spatial genomics data2022

    • Author(s)
      Vandenbon Alexis、Diez Diego
    • Journal Title

      bioRxiv

      Volume: -

    • DOI

      10.1101/2022.11.13.516355

    • Related Report
      2022 Annual Research Report
    • Open Access
  • [Journal Article] Guidance for RNA-seq co-expression estimates: the importance of data normalization, batch effects, and correlation measures2021

    • Author(s)
      Vandenbon Alexis
    • Journal Title

      bioRxiv

      Volume: -

    • DOI

      10.1101/2021.03.11.435043

    • Related Report
      2020 Research-status Report
    • Open Access
  • [Presentation] Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data2022

    • Author(s)
      Alexis Vandenbon
    • Organizer
      第11回生命医薬情報学連合大会
    • Related Report
      2022 Annual Research Report

URL: 

Published: 2020-04-28   Modified: 2024-01-30  

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