Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods
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Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods. / Moore, John P.; Gao, Yu; Zietsman, Anscha J.J.; Fangel, Jonatan U.; Trygg, Johan; Willats, William G.T.; Vivier, Melané A.
The Plant Cell Wall. ed. / Zoë A. Popper. Humana Press, 2020. p. 327-337 (Methods in Molecular Biology, Vol. 2149).Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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TY - CHAP
T1 - Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods
AU - Moore, John P.
AU - Gao, Yu
AU - Zietsman, Anscha J.J.
AU - Fangel, Jonatan U.
AU - Trygg, Johan
AU - Willats, William G.T.
AU - Vivier, Melané A.
N1 - Publisher Copyright: © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020
Y1 - 2020
N2 - Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant–pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography–mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).
AB - Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant–pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography–mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).
KW - Cell wall profiling
KW - CoMPP
KW - FT-IR
KW - GC-MS
KW - Multivariate data analysis
U2 - 10.1007/978-1-0716-0621-6_18
DO - 10.1007/978-1-0716-0621-6_18
M3 - Book chapter
C2 - 32617943
AN - SCOPUS:85087473292
SN - 978-1-0716-0619-3
T3 - Methods in Molecular Biology
SP - 327
EP - 337
BT - The Plant Cell Wall
A2 - null, Zoë A. Popper
PB - Humana Press
ER -
ID: 270665637