title: Metabolomic Biomarker Identification in Presence of Outliers and Missing Values.
abstract: Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition,t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease.
authors.affiliation: Bioinformatics Lab, Department of Statistics, Rajshahi University, Rajshahi, Bangladesh; Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh.
authors.forename: Nishith
authors.identifier: 0000-0002-1911-4775
authors.initials: N
authors.lastname: Kumar
authors.affiliation: Bioinformatics Lab, Department of Statistics, Rajshahi University, Rajshahi, Bangladesh.
authors.forename: Md Aminul
authors.initials: MA
authors.lastname: Hoque
authors.affiliation: Bioinformatics Lab, Department of Statistics, Rajshahi University, Rajshahi, Bangladesh; Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh.
authors.forename: Md
authors.initials: M
authors.lastname: Shahjaman
authors.affiliation: Institute of Biological Sciences, Rajshahi University, Rajshahi, Bangladesh.
authors.forename: S M Shahinul
authors.initials: SM
authors.lastname: Islam
authors.affiliation: Bioinformatics Lab, Department of Statistics, Rajshahi University, Rajshahi, Bangladesh.
authors.forename: Md Nurul Haque
authors.initials: MN
authors.lastname: Mollah
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis / Q000378:metabolism*; D019295:Computational Biology; D016208:Databases, Factual; D005189:False Positive Reactions; D008401:Gas Chromatography-Mass Spectrometry; D056726:Genetic Association Studies; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D055432:Metabolomics; D015233:Models, Statistical; D011379:Prognosis; D012372:ROC Curve
chemical_list: D014408:Biomarkers, Tumor