2022 Fiscal Year Final Research Report
Comprehensive optimization of cell type-specific gene co-expression networks and construction of a cell type-specific co-expression database
| Project/Area Number |
20K06609
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 43060:System genome science-related
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | bioinformatics / gene expression / gene co-expression / data normalization / batch effect correction / database |
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| 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.
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| Free Research Field |
bioinformatics
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| 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.
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