JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES) ›› 2015, Vol. 53 ›› Issue (2): 92-96.doi: 10.6040/j.issn.1671-7554.0.2014.476

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Serum metabolic profiling of schizophrenia based on random forest

LIU Yingjun1, ZHANG Tao1, WANG Lu2, LIU Jia2, CHANG Xuerun2, ZHANG Jingxuan2, XUE Fuzhong1   

  1. 1. Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    2. Shandong Mental Health Center, Jinan 250014, Shandong, China
  • Received:2014-07-21 Published:2015-02-10

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Abstract: Objective To explore the classification ability of random forest in the serum metabolic profiling of schizophrenia patients and healthy controls and to select significant metabolites. Methods The case group consisted of 50 patients with schizophrenia and control group consisted of 62 healthy individuals. The serum samples of case and control groups were collected and detected by RRLC-QTOF/MS platform. Random forest was used to classify the serum metabolic data in case and control groups. OOB estimate of error rate and 5 fold cross validation were used to evaluate the classification ability. In addition, variable importance measure of random forest was adopted to select important metabolites. Results Schizophrenia and control serum metabolic data could be classified well using the method of random forest. The misclassification rates in case and control groups were 4.0% and 1.6% respectively, OOB estimate of error rate was 2.68%, and the area under the curve of ROC was 0.99. Furthermore,15 important metabolites were selected according to variable importance measure. Conclusion The combination of liquid chromatography-mass spectrum technology with random forest can select metabolites with potential clinical application value, and be used in the study of metabolomics.

Key words: Metabolomics, Classification, Schizophrenia, Random forest, Variable selection

CLC Number: 

  • R749.3
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