Multiblock metabolomics: An approach to elucidate whole-body metabolism with multiblock principal component analysis
Tanabe et al., Comput Struct Biotechnol J., 2021;19:1956-1965.
【発表概要】
「サンプル×バイオマーカー」の２次元に集約されたオミクスデータを、直感的かつ視覚的にその結果を表すために「主成分分析（ＰＣＡ）」が広く用いられます。しかしこれら２次元データに、さらにタイムコースや組織分布情報が加わりデータ構造が３次元になった場合に、ＰＣＡの主成分軸が一義的に定まらず、解析が困難になることがございました。本論文ではケモメトリクス分野で応用される「マルチブロックＰＣＡ」をメタボロミクスへ応用し、複数臓器の解析した結果を紹介します。
【Summary】
Principal component analysis (PCA) is a useful tool for omics analysis to identify underlying factors and visualize relationships between biomarkers. However, this approach is limited in addressing life complexity and further improvement is required. This study aimed to develop a new approach that combines mass spectrometry-based metabolomics with multiblock PCA to elucidate the whole-body global metabolic network, thereby generating comparable metabolite maps to clarify the metabolic relationships among several organs. To evaluate the newly developed method, Zucker diabetic fatty (ZDF) rats (n = 6) were used as type 2 diabetic models and Sprague Dawley (SD) rats (n = 6) as controls. When the metabolites obtained from the three organs were analyzed with multiblock PCA, the score and loading maps obtained were highly synchronized and their metabolism patterns were visually comparable.
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