Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Yamamoto, Hiroyuki ^a   Fujimori, Tamaki ^a   Sato, Hajime ^a   Ishikawa, Gen ^a   Kami, Kenjiro ^a   Ohashi, Yoshiaki ^a  

^a Keio University and Human Metabolome Technologies Inc   (Japan)

Author keywords

Metabolite set enrichment analysis; Principal component analysis; Statistical hypothesis testing of factor loading

Indexed keywords

BIOMOLECULES; FACTOR ANALYSIS; METABOLITES; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL TESTS; STATISTICAL METHODS; STATISTICS; TESTING;

CORRELATION COEFFICIENT; HIGH-DIMENSIONAL; ITS APPLICATIONS; METABOLIC PATHWAYS; METABOLITE LEVEL; METABOLOMICS; MOUSE LIVER; STATISTICAL HYPOTHESIS TESTING; DIMENSIONAL SUBSPACE; STATISTICAL CRITERION;

LOADING; PRINCIPAL COMPONENT ANALYSIS;

ANIMAL; ARTICLE; CHEMISTRY; LIVER; METABOLISM; METABOLOME; METABOLOMICS; METHODOLOGY; MOUSE; PRINCIPAL COMPONENT ANALYSIS; TRANSGENIC MOUSE;

ANIMALS; LIVER; METABOLIC NETWORKS AND PATHWAYS; METABOLOME; METABOLOMICS; MICE; MICE, TRANSGENIC; PRINCIPAL COMPONENT ANALYSIS;

EID: 84899491622     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-51     Document Type: Article

References (33)

1. Lavine B, Workman J. Chemometrics. Anal Chem 2010, 82(12):4699-4711. 10.1021/ac101202z, 20481510.
2. Jolliffe IT. Principal component analysis 2002, New York: Springer-Verlag, 2.
3. Barker M, Rayens W. Partial least squares for discrimination. J Chemometr 2003, 17(3):166-173.
4. Yamamoto H, Yamaji H, Fukusaki E, Ohno H, Fukuda H. Canonical correlation analysis for multivariate regression and its application to metabolic fingerprinting. Biochem Eng J 2007, 40:199-204.
5. Ringnér M. What is principal component analysis?. Nat Biotechnol 2008, 26:303-304. 10.1038/nbt0308-303, 18327243.
6. Landgrebe J, Wurst W, Welzl G. Permutation-validated principal components analysis of microarray data. Genome Biol 2002, 3(4):1-11.
7. Dileo MV, Strahan GD, den Bakker M, Hoekenga OA. Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome. PLoS One 2011, 6(10):e26683. 10.1371/journal.pone.0026683, 3198806, 22039529.
8. Dewar BJ, Keshari K, Jeffries R, Dzeja P, Graves LM, Macdonald JM. Metabolic assessment of a novel chronic myelogenous leukemic cell line and an imatinib resistant subline by H NMR spectroscopy. Metabolomics 2010, 6(3):439-450. 10.1007/s11306-010-0204-0, 2899017, 20676217.
9. Maruyama K, Takeda M, Kidokoro S, Yamada K, Sakuma Y, Urano K, Fujita M, Yoshiwara K, Matsukura S, Morishita Y, Sasaki R, Suzuki H, Saito K, Shibata D, Shinozaki K, Yamaguchi-Shinozaki K. Metabolic pathways involved in cold acclimation identified by integrated analysis of metabolites and transcripts regulated by DREB1A and DREB2A. Plant Physiol 2009, 150(4):1972-1980. 10.1104/pp.109.135327, 2719109, 19502356.
10. Van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006, 7:142. 10.1186/1471-2164-7-142, 1534033, 16762068.
11. Afifi A, May S, Clark VA, Practical Multivariate Analysis 2011, 364-366. London: Chapman and Hall/CRC, 5, Practical Multivariate Analysis.
12. Pedro RP, Donald AJ, Keith MS. Giving meaningful interpretation to ordination axes: assessing loading significance in principal component analysis. Ecology 2003, 84:2347-2363.
13. Xia J, Wishart DS. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 2010, 38(Web Server issue):W71-W77. 2896187, 20457745.
14. Xia J, Wishart DS. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protoc 2011, 6:743-760.15. 10.1038/nprot.2011.319, 21637195.
15. Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA. Global function profiling of gene expression. Genomics 2003, 81:98-104. 10.1016/S0888-7543(02)00021-6, 12620386.
16. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102(43):15545-15550. 10.1073/pnas.0506580102, 1239896, 16199517.
17. Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 2004, 20(1):93-99. 10.1093/bioinformatics/btg382, 14693814.
18. Ooga T, Sato H, Nagashima A, Sasaki K, Tomita M, Soga T, Ohashi Y. Metabolomic anatomy of an animal model revealing homeostatic imbalances in dyslipidaemia. Mol Biosyst 2011, 7(4):1217-1223. 10.1039/c0mb00141d, 21258713.
19. Soga T, Heiger DN. Amino acid analysis by capillary electrophoresis electrospray ionization mass spectrometry. Anal Chem 2000, 72:1236-1241. 10.1021/ac990976y, 10740865.
20. Soga T, Ueno Y, Naraoka H, Ohashi Y, Tomita M, Nishioka T. Analysis of nucleotides by pressure-assisted capillary electrophoresis mass spectrometry using silanol mask technique. J Chromatogr A 2007, 1159:125-133. 10.1016/j.chroma.2007.05.054, 17543971.
21. Sugimoto M, Wong D, Hirayama A, Soga T, Tomita M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identifies oral, breast and pancreatic cancer-specific profiles. Metabolomics 2010, 6:78-95. 10.1007/s11306-009-0178-y, 2818837,2818837, 20300169.
22. R Development Core Team R: A language and environment for statistical computing 2005, Vienna, Austria: R Foundation for Statistical Computing, R Development Core Team.
23. mseapca: Metabolite set enrichment analysis for factor loading in principal component analysis http://cran.r-project.org/web/packages/mseapca/, mseapca: Metabolite set enrichment analysis for factor loading in principal component analysis.
24. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30. 10.1093/nar/28.1.27, 102409, 10592173.
25. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995, 57(1):289-300.
26. Subramanian A, Kuehn H, Gould J, Tamayo P, Mesirov JP. GSEA-P: a desktop application for gene Set enrichment analysis. Bioinformatics 2007, 23(23):3251-3253. 10.1093/bioinformatics/btm369, 17644558.
27. Sharma K, McCue P, Dunn SR. Diabetic kidney disease in the db/db mouse. Am J Physiol Renal Physiol 2003, 284:F1138-F1144.
28. Kemnitz JW, Elson DF, Roecker EB, Baum ST, Bergman RN, Maglasson MD. Pioglitazone increases insulin sensitivity, reduces blood glucose, insulin, and lipid levels, and lowers blood pressure, in obese, insulin-resistant rhesus monkeys. Diabetes 1994, 43:204-211. 10.2337/diab.43.2.204, 8288044.
29. Smith U. Pioglitazone: mechanism of action. Int J Clin Pract 2001, 121:13-18.
30. Lee CH, Olson P, Hevener A, Mehl I, Chong LW, Olefsky JM, Gonzalez FJ, Ham J, Kang H, Peters JM, Evans RM. PPARδ regulates glucose metabolism and insulin sensitivity. Proc Natl Acad Sci U S A 2006, 103(9):3444-3449. 10.1073/pnas.0511253103, 1413918, 16492734.
31. Yamamoto H, Yamaji H, Abe Y, Harada K, Waluyo D, Fukusaki E, Kondo A, Ohno H, Fukuda H. Dimensionality reduction for metabolome data using PCA, PLS, OPLS, and RFDA with differential penalties to latent variables. Chemom Intell Lab Syst 2009, 98(2):136-142.
32. Timmerman ME, van der Greef J, Lamers RAN, Huub C, Hoefsloot J, Smilde AK, Jansen JJ. ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics 2005, 21(13):3043-3048. 10.1093/bioinformatics/bti476, 15890747.
33. Fehrmann RSN, de Jonge HJM,Elst A, de Vries A, Crijns AGP, Weidenaar AC, Gerbens F, de Jong S, van der Zee AGJ, de Vries EGE, Kamps WA, Hofstra RMW,Meerman GJ, de Bont ESJM. A New perspective on transcriptional system regulation (TSR): towards TSR profiling. PLoS One 2008, 3(2):e1656. 10.1371/journal.pone.0001656, 2250855, 18297136.