2023 Fiscal Year Final Research Report
scRNA-seq analysis using PCA and TD based unsupervised feature extraction
| Project/Area Number |
20K12067
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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 62010:Life, health and medical informatics-related
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| Research Institution | Chuo University |
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Principal Investigator |
Taguchi Y-h. 中央大学, 理工学部, 教授 (30206932)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | テンソル分解 / 教師なし学習 / 変数選択 |
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| Outline of Final Research Achievements |
The effectiveness of the tensor decomposition method for the analysis of single cell multimics was confirmed and published in scientific papers and as research reports in international conferences. In particular, the method was found to be effective for integrated analysis of gene expression profiles, methylation and ATAC-seq.This is expected to facilitate the use of tensor decomposition for single-cell analysis in the future. This is a very valuable research result, as such research has not been done before.
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| Free Research Field |
バイオインフォマティクス
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| Academic Significance and Societal Importance of the Research Achievements |
この方法の開発により一細胞解析を教師なし学習で行う道が開けた。教師なし学習は人間の偏見から自由に結果を出すことができるので非常に貴重な成果であると言える。
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