A modified k-TSP algorithm and its application in LC–MS-based metabolomics study of hepatocellular carcinoma and chronic liver diseases

•A modified k top scoring pairs (k-TSP) method is suggested to provide an improved classification procedure.•This new k-TSP method was applied to serum metabolomics data derived from LC–MS of liver diseases.•The metabolic feature pairs can be effectively used to differentiate HCC from chronic liver...

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Veröffentlicht in:Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Jg. 966; S. 100 - 108
Hauptverfasser: Lin, Xiaohui, Gao, Jiuchong, Zhou, Lina, Yin, Peiyuan, Xu, Guowang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.09.2014
Schlagworte:
Algorithms
blood serum
Carcinoma, Hepatocellular - blood
Carcinoma, Hepatocellular - metabolism
Chromatography
Chromatography, Liquid - methods
Classification
data collection
Diseases
Feature selection
genomics
hepatoma
Humans
LC–MS
liquid chromatography
Liver
Liver diseases
Liver Diseases - blood
Liver Diseases - metabolism
Liver Neoplasms - blood
Liver Neoplasms - metabolism
mass spectrometry
Mass Spectrometry - methods
Metabolome
Metabolomics
Metabolomics - methods
Principal component analysis
Top scoring pairs
TSP
TSP
Metabolomics
Feature selection
Liver diseases
Top scoring pairs
LC–MS
ISSN:1570-0232, 1873-376X, 1873-376X
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Zusammenfassung:•A modified k top scoring pairs (k-TSP) method is suggested to provide an improved classification procedure.•This new k-TSP method was applied to serum metabolomics data derived from LC–MS of liver diseases.•The metabolic feature pairs can be effectively used to differentiate HCC from chronic liver diseases. In systems biology, the ability to discern meaningful information that reflects the nature of related problems from large amounts of data has become a key issue. The classification method using top scoring pairs (TSP), which measures the features of a data set in pairs and selects the top ranked feature pairs to construct the classifier, has been a powerful tool in genomics data analysis because of its simplicity and interpretability. This study examined the relationship between two features, modified the ranking criteria of the k-TSP method to measure the discriminative ability of each feature pair more accurately, and correspondingly, provided an improved classification procedure. Tests on eight public data sets showed the validity of the modified method. This modified k-TSP method was applied to our serum metabolomics data derived from liquid chromatography-mass spectrometry analysis of hepatocellular carcinoma and chronic liver diseases. Based on the 27 selected feature pairs, HCC and chronic liver diseases were accurately distinguished using the principal component analysis, and certain profound metabolic disturbances related to liver disease development were revealed by the feature pairs.
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ISSN:1570-0232
1873-376X
1873-376X
DOI:10.1016/j.jchromb.2014.05.044