Abstract
The post-Genomic Era is characterized by the proliferation of high-throughput platforms that allow the parallel study of a complete body of molecules in one single run of experiments (omic approach). Analysis and integration of omic data represent one of the most challenging frontiers for all the disciplines related to Systems Biology. From the computational perspective this requires, among others, the massive use of automated approaches in several steps of the complex analysis pipeline, often consisting of cascades of statistical tests. In this frame, the identification of statistical significance has been one of the early challenges in the handling of omic data and remains a critical step due to the multiple hypotheses testing issue, given the large number of hypotheses examined at one time. Two main approaches are currently used: p-values based on random permutation approaches and the False Discovery Rate. Both give meaningful and important results, however they suffer respectively from being computationally heavy -due to the large number of data that has to be generated-, or extremely flexible with respect to the definition of the significance threshold, leading to difficulties in standardization. We present here a complementary/alternative approach to these current ones and discuss performances, properties and limitations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nardini, C., Benini, L., Micheli, G.D.: Circuits and systems for high-throughput biology. Circuits and Systems Magazine, IEEE 6(3), 10–20 (2006)
Ramaswamy, S., Ross, K.N., Lander, E.S., Golub, T.R.: A molecular signature of metastasis in primary solid tumors. Nat. Genet. 33(1), 49–54 (2003)
Lapointe, J., Li, C., Higgins, J.P., van de Rijn, M., Bair, E., Montgomery, K., Ferrari, M., Egevad, L., Rayford, W., Bergerheim, U., Ekman, P., DeMarzo, A.M., Tibshirani, R., Botstein, D., Brown, P.O., Brooks, J.D., Pollack, J.R.: Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc. Natl. Acad. Sci. 101, 811–816 (2004)
Rossi, S., Masotti, D., Nardini, C., Bonora, E., Romeo, G., Macii, E., Benini, L., Volinia, S.: TOM: a web-based integrated approach for efficient identification of candidate disease genes. Nucleic Acids Res. 34, 285–292 (2006)
Segal, E., Sirlin, C.B., Ooi, C., Adler, A.S., Gollub, J., Chen, X., Chan, B.K., Matcuk, G., Barry, C., Chang, H.Y., Kuo, M.D.: Decoding global gene expression programs in liver cancer by noninvasive imaging. Nature Biotechnology 25, 675–680 (2007)
Diehn, M., Nardini, C., Wang, D.S., McGovern, S., Jayaraman, M., Liang, Y., Aldape, K., Cha, S., Kuo, M.D.: Identification of non-invasive imaging surrogates for brain tumor gene expression modules. PNAS 105(13), 5213–5218 (2008)
Sokal, R.R., Rohlf, F.J.: Biometry. Freeman, New York (2003)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995)
Storey, J.D., Tibshirani, R.: Statistical significance for genomewide studies. PNAS 10(16), 9440–9445 (2003)
Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. 98, 5116–5121 (2001)
Tiffin, N., Adie, E., Turner, F., Brunner, H., van Drielnd, M., Oti, M.A., Lopez-Bigas, N., Ouzunis, C., Perez-Iratxeta, C., Andrade-Navarro, M.A., Adeyemo, A., Patti, M.E., Semple, C.A.M., Hide, W.: Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. Nucleic Acids Res. 34 (2006)
Hedges, L.B., Olkin, I.: Statistical Methods in Meta-Analysis. Academic Press, New York (1985)
Pan, K.H., Lih, C.J., Cohen, S.N.: Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays. Proc. Natl. Acad. Sci. 102(25), 8961–8965 (2005)
Gentleman, R., Carey, V., Huber, W., Irizarry, R., Dudoit, S.: Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, Heidelberg (2005)
Cheng, C., Pounds, S., Boyett, J., Pei, D., Kuo, M., Roussel, M.F.: Statistical significance threshold criteria for analysis of microarray gene expression data. Stat. Appl. Genet. Mol. Biol. 3, Article36 (2004)
Yang, J.J., Yang, M.C.: An improved procedure for gene selection from microarray experiments using false discovery rate criterion. BMC Bioinformatics 7, 15 (2006)
Liang, Y., Diehn, M., Watson, N., Bollen, A.W., Aldape, K.D., Nicholas, M.K., Lamborn, K.R., Berger, M.S., Botstein, D., Brown, P.O., Israel, M.A.: Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. Proc. Natl. Acad. Sci. 102(16), 5814–5819 (2005)
Bansal, M., Belcastro, V., Ambesi-Impiombato, A., di Bernardo, D.: How to infer gene networks from expression profiles. Mol. Syst. Biol. 3 (2007)
Watts, D.J., Strogatz, S.: Collective dynamics of ’small-world’ networks. Nature 393, 440–442 (1998)
Lauritzen, S.L.: Graphical Models. Oxford University Press, New York (1996)
The Gene Ontology Consortium. Creating the gene ontology resource: Design and implementation. Genome Res. 11(8), 1425–1433 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nardini, C., Wang, L., Peng, H., Benini, L., Kuo, M.D. (2008). MM-Correction: Meta-analysis-Based Multiple Hypotheses Correction in Omic Studies. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2008. Communications in Computer and Information Science, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92219-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-92219-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92218-6
Online ISBN: 978-3-540-92219-3
eBook Packages: Computer ScienceComputer Science (R0)