Classification method for heterogeneity in monoclonal cell population

S. Aburatani, Kosuke Tashiro, S. Kuhara

研究成果: ジャーナルへの寄稿Conference article

抄録

Monoclonal cell populations are known to be composed of heterogeneous subpopulations, thus complicating the data analysis. To gain clear insights into the mechanisms of cellular systems, biological data from a homogeneous cell population should be obtained. In this study, we developed a method based on Latent Profile Analysis (LPA) combined with Confirmatory Factor Analysis (CFA) to divide mixed data into classes, depending on their heterogeneity. In general cluster analysis, the number of measured points is a constraint, and thereby the data must be classified into fewer groups than the number of samples. By our newly developed method, the measured data can be divided into groups depending on their latent effects, without constraints. Our method is useful to clarify all types of omics data, including transcriptome, proteome and metabolic information.

元の言語英語
記事番号012077
ジャーナルJournal of Physics: Conference Series
633
発行部数1
DOI
出版物ステータス出版済み - 9 21 2015
イベント4th International Conference on Mathematical Modeling in Physical Sciences, IC-MSquare 2015 - Mykonos, ギリシャ
継続期間: 6 5 20156 8 2015

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proteome
cluster analysis
factor analysis
profiles

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

これを引用

Classification method for heterogeneity in monoclonal cell population. / Aburatani, S.; Tashiro, Kosuke; Kuhara, S.

:: Journal of Physics: Conference Series, 巻 633, 番号 1, 012077, 21.09.2015.

研究成果: ジャーナルへの寄稿Conference article

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