TY - CHAP
T1 - Data Processing and Analysis in Liquid Chromatography–Mass Spectrometry-Based Targeted Metabolomics
AU - Sugimoto, Masahiro
AU - Aizawa, Yumi
AU - Tomita, Atsumi
N1 - Funding Information:
This research was funded by grants from JSPS KAKENHI (grant number 20B205) and JST OPERA (grant number JPMJOP1842).
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023
Y1 - 2023
N2 - Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.
AB - Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.
KW - Data processing
KW - Mass spectrometry
KW - Multivariate analysis
KW - Statistical analysis
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U2 - 10.1007/978-1-0716-2699-3_21
DO - 10.1007/978-1-0716-2699-3_21
M3 - Chapter
C2 - 36152165
AN - SCOPUS:85138458055
T3 - Methods in Molecular Biology
SP - 241
EP - 255
BT - Methods in Molecular Biology
PB - Humana Press Inc.
ER -