Principal component and hierarchical clustering analysis of metabolites in destructive weeds; polygonaceous plants

被引:0
|
作者
Atsuko Miyagi
Hideyuki Takahashi
Kentaro Takahara
Takayuki Hirabayashi
Yoshiki Nishimura
Takafumi Tezuka
Maki Kawai-Yamada
Hirofumi Uchimiya
机构
[1] The University of Tokyo,Institute of Molecular and Cellular Biosciences
[2] Iwate Biotechnology Research Center,Graduate School of Science
[3] Kyoto University,School of Health and Human Life
[4] Nagoya Bunri University,Department of Environmental Science and Human Engineering
[5] Saitama University,Core Research for Evolutional Science and Technology (CREST)
[6] Japan Science and Technology Agency (JST),undefined
来源
Metabolomics | 2010年 / 6卷
关键词
Polygonaceae; Metabolite profile; Capillary electrophoresis–mass spectrometry; Oxalate; Principal component analysis; Hierarchical clustering analysis;
D O I
暂无
中图分类号
学科分类号
摘要
Comprehensive analysis of metabolites using capillary electrophoresis–mass spectrometry was carried out in harmful weeds belonging to Polygonaceae. A principal component analysis revealed clear distinctions among eight Rumex species and Fallopia japonica. Hierarchical clustering data showed that respective metabolites can be grouped due to species differences. There was a positive relationship between oxalate and citrate, oxalate and ascorbate, and oxalate and glutamine. The amount of oxalate per leaf fresh weight was not affected by increased concentrations of exogenously supplied nutrients from Hoagland’s formulation in one of the most destructive weeds R. obtusifolius. The oxalate accumulation in this plant is independent of external nutrient level, where nutrient-rich environments apparently stimulate internal constituents such as amino acids and other metabolites.
引用
收藏
页码:146 / 155
页数:9
相关论文
共 50 条
  • [31] Principal component analysis for clustering gene expression data
    Yeung, KY
    Ruzzo, WL
    BIOINFORMATICS, 2001, 17 (09) : 763 - 774
  • [32] Effect of dimension reduction by principal component analysis on clustering
    Erisoglu, Murat
    Erisoglu, Ulku
    JOURNAL OF STATISTICS AND MANAGEMENT SYSTEMS, 2011, 14 (02) : 277 - 287
  • [33] A random version of principal component analysis in data clustering
    Palese, Luigi Leonardo
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2018, 73 : 57 - 64
  • [34] Distributed Clustering Using Collective Principal Component Analysis
    Hillol Kargupta
    Weiyun Huang
    Krishnamoorthy Sivakumar
    Erik Johnson
    Knowledge and Information Systems, 2001, 3 (4) : 422 - 448
  • [35] Samples clustering and recognition with fuzzy clustering and principal component analysis method in spectral analysis
    Chu, XL
    Yuan, HF
    Lu, WZ
    CHINESE JOURNAL OF ANALYTICAL CHEMISTRY, 2000, 28 (04) : 421 - 427
  • [36] Local independent component analysis with fuzzy clustering and regression-principal component analysis
    Maenaka, Tatsuya
    Honda, Katsuhiro
    Ichihashi, Hidetomo
    2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, 2006, : 857 - +
  • [37] Classification of fentanyl analogues through principal component analysis (PCA) and hierarchical clustering of GC-MS data
    Gilbert, Nicolas
    Mewis, Ryan E.
    Sutcliffe, Oliver B.
    FORENSIC CHEMISTRY, 2020, 21
  • [38] A Comparison of Antioxidant, Antibacterial, and Anticancer Activity of the Selected Thyme Species by Means of Hierarchical Clustering and Principal Component Analysis
    Orlowska, M.
    Pytlakowska, K.
    Mrozek-Wilczkiewicz, A.
    Musiol, R.
    Waksmundzka-Hajnos, M.
    Sajewicz, M.
    Kowalska, T.
    ACTA CHROMATOGRAPHICA, 2016, 28 (02) : 207 - 221
  • [39] Rootlets Hierarchical Principal Component Analysis for Revealing Nested Dependencies in Hierarchical Data
    Wylie, Korey P.
    Tregellas, Jason R.
    MATHEMATICS, 2025, 13 (01)
  • [40] Analysis of breast cancer progression using principal component analysis and clustering
    G. Alexe
    G. S. Dalgin
    S. Ganesan
    C. DeLisi
    G. Bhanot
    Journal of Biosciences, 2007, 32 : 1027 - 1039