全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
Microarrays  2013 

Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

DOI: 10.3390/microarrays2020131

Keywords: gene expression microarray, normalization, Illumina

Full-Text   Cite this paper   Add to My Lib

Abstract:

While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation—if not adequately minimized by effective normalization—may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes.

References

[1]  Michael, K.L.; Taylor, L.C.; Schultz, S.L.; Walt, D.R. Randomly ordered addressable high-density optical sensor arrays. Anal. Chem. 1998, 70, 1242–1248, doi:10.1021/ac971343r.
[2]  Oliphant, A.; Barker, D.L.; Stuelpnagel, J.R.; Chee, M.S. BeadArray technology: Enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques 2002, 56–58, 60–61.
[3]  Fan, J.B.; Yeakley, J.M.; Bibikova, M.; Chudin, E.; Wickham, E.; Chen, J.; Doucet, D.; Rigault, P.; Zhang, B.; Shen, R.; et al. A versatile assay for high-throughput gene expression profiling on universal array matrices. Genome Res. 2004, 14, 878–885, doi:10.1101/gr.2167504.
[4]  Gunderson, K.L.; Kruglyak, S.; Graige, M.S.; Garcia, F.; Kermani, B.G.; Zhao, C.; Che, D.; Dickinson, T.; Wickham, E.; Bierle, J.; et al. Decoding randomly ordered DNA arrays. Genome Res. 2004, 14, 870–877, doi:10.1101/gr.2255804.
[5]  Kuhn, K.; Baker, S.C.; Chudin, E.; Lieu, M.H.; Oeser, S.; Bennett, H.; Rigault, P.; Barker, D.; McDaniel, T.K.; Chee, M.S. A novel, high-performance random array platform for quantitative gene expression profiling. Genome Res. 2004, 14, 2347–2356, doi:10.1101/gr.2739104.
[6]  Stokes, T.H.; Han, X.; Moffitt, R.A.; Wang, M.D. Extending Microarray Quality Control and Analysis Algorithms to Illumina Chip Platform. In Proceedings of the IEEE 29th Annual International Conference, Lyon, France, 22–26 August 2007; pp. 4637–4640.
[7]  Shi, L.; Reid, L.H.; Jones, W.D.; Shippy, R.; Warrington, J.A.; Baker, S.C.; Collins, P.J.; de Longueville, F.; Kawasaki, E.S.; Lee, K.Y.; et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 2006, 24, 1151–1161.
[8]  Shippy, R.; Fulmer-Smentek, S.; Jensen, R.V.; Jones, W.D.; Wolber, P.K.; Johnson, C.D.; Pine, P.S.; Boysen, C.; Guo, X.; Chudin, E.; et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat. Biotechnol. 2006, 24, 1123–1131.
[9]  Chen, J.J.; Hsueh, H.M.; Delongchamp, R.R.; Lin, C.J.; Tsai, C.A. Reproducibility of microarray data: A further analysis of Microarray Quality Control (MAQC) data. BMC Bioinform. 2007, 8, 412, doi:10.1186/1471-2105-8-412.
[10]  Maouche, S.; Poirier, O.; Godefroy, T.; Olaso, R.; Gut, I.; Collet, J.P.; Montalescot, G.; Cambien, F. Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells. BMC Genomics 2008, 9, 302, doi:10.1186/1471-2164-9-302.
[11]  Asare, A.L.; Gao, Z.; Carey, V.J.; Wang, R.; Seyfert-Margolis, V. Power enhancement via multivariate outlier testing with gene expression arrays. Bioinformatics 2009, 25, 48–53.
[12]  Du, P.; Kibbe, W.A.; Lin, S.M. Lumi: A pipeline for processing Illumina microarray. Bioinformatics 2008, 24, 1547–1548.
[13]  Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 2004, 3, doi:10.2202/1544-6115.1027.
[14]  Bioconductor. Available online: http://www.bioconductor.org (accessed on 13 May 2013).
[15]  Schmid, R.; Baum, P.; Ittrich, C.; Fundel-Clemens, K.; Huber, W.; Brors, B.; Eils, R.; Weith, A.; Mennerich, D.; Quast, K. Comparison of normalization methods for Illumina BeadChip HumanHT-12 v3. BMC Genomics 2010, 11, 349, doi:10.1186/1471-2164-11-349.
[16]  Dunning, M.J.; Smith, M.L.; Ritchie, M.E.; Tavare, S. Beadarray: R classes and methods for Illumina bead-based data. Bioinformatics 2007, 23, 2183–2184.
[17]  Dunning, M.J.; Barbosa-Morais, N.L.; Lynch, A.G.; Tavare, S.; Ritchie, M.E. Statistical issues in the analysis of Illumina data. BMC Bioinform. 2008, 9, 85, doi:10.1186/1471-2105-9-85.
[18]  Dunning, M.J.; Ritchie, M.E.; Barbosa-Morais, N.L.; Tavare, S.; Lynch, A.G. Spike-in validation of an Illumina-specific variance-stabilizing transformation. BMC Res. Notes 2008, 18, doi:10.1186/1756-0500-1-18.
[19]  Workman, C.; Jensen, L.J.; Jarmer, H.; Berka, R.; Gautier, L.; Nielser, H.B.; Saxild, H.H.; Nielsen, C.; Brunak, S.; Knudsen, S. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol. 2002, 3, doi:10.1186/gb-2002-3-9-research0048.
[20]  Bolstad, B.M.; Irizarry, R.A.; Astrand, M.; Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19, 185–193.
[21]  Reiner, A.; Yekutieli, D.; Benjamini, Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003, 19, 368–375.
[22]  Rothman, K.J. No adjustments are needed for multiple comparisons. Epidemiology 1990, 1, 43–46, doi:10.1097/00001648-199001000-00010.
[23]  Bender, R.; Lange, S. Adjusting for multiple testing—When and how? J. Clin. Epidemiol. 2001, 54, 343–349, doi:10.1016/S0895-4356(00)00314-0.
[24]  Cotta, C.; Sloper, C.; Moscato, P. Evolutionary search of thresholds for robust feature set selection: Application to the analysis of microarray data. In Applications of Evolutionary Computing; Raidl, G.R., Ed.; Springer: Berlin, Germany, 2004; pp. 21–30.
[25]  Cotta, C.; Langston, M.A.; Moscato, P. Combinatorial and algorithmic issues for microarray analysis. In Handbook of Approximation Algorithms and Metaheuristics; Gonzalez, T.F., Ed.; Chapman & Hall/CRC: London, UK, 2007; pp. 74:1–74:14.
[26]  Gomez Ravetti, M.; Moscato, P. Identification of a 5-protein biomarker molecular signature for predicting Alzheimer’s disease. PLoS One 2008, 3, e3111, doi:10.1371/journal.pone.0003111.
[27]  Berretta, R.; Costa, W.; Moscato, P. Combinatorial optimization models for finding genetic signatures from gene expression datasets. Methods Mol. Biol. 2008, 453, 363–377, doi:10.1007/978-1-60327-429-6_19.
[28]  Rodriguez, A.; Hilvo, M.; Kytomaki, L.; Fleming, R.E.; Britton, R.S.; Bacon, B.R.; Parkkila, S. Effects of iron loading on muscle: Genome-wide mRNA expression profiling in the mouse. BMC Genomics 2007, 8, 379, doi:10.1186/1471-2164-8-379.
[29]  Johnstone, D.; Milward, E.A. Genome-wide microarray analysis of brain gene expression in mice on a short-term high iron diet. Neurochem. Int. 2010, 56, 856–863, doi:10.1016/j.neuint.2010.03.015.
[30]  Drake, S.F.; Morgan, E.H.; Herbison, C.E.; Delima, R.; Graham, R.M.; Chua, A.C.; Leedman, P.J.; Fleming, R.E.; Bacon, B.R.; Olynyk, J.K.; et al. Iron absorption and hepatic iron uptake are increased in a transferrin receptor 2 (Y245X) mutant mouse model of hemochromatosis type 3. Am. J. Physiol. Gastrointest. Liver Physiol. 2007, 292, G323–G328.
[31]  Illumina (2008) GenomeStudio Gene Expression Module v1.0 User Guide. Available online: http://support.illumina.com/documents/MyIllumina/c94519f7-9348-4308-a32f-b66ff3959e99/GenomeStudio_GX_Module_v1.0_UG_11319121_RevA.pdf (accessed on 15 May 2013).
[32]  Fayyad, U.M.; Irani, K.B. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France, 28 August–3 September 1993; Bajcsw, R., Ed.; Morgan Kaufmann: San Francisco, CA, USA, 1993; pp. 1022–1029.
[33]  Ritchie, M.E.; Dunning, M.J.; Smith, M.L.; Shi, W.; Lynch, A.G. BeadArray expression analysis using bioconductor. PLoS Comput. Biol. 2011, 7, e1002276, doi:10.1371/journal.pcbi.1002276.
[34]  Barbacioru, C.C.; Wang, Y.; Canales, R.D.; Sun, Y.A.; Keys, D.N.; Chan, F.; Poulter, K.A.; Samaha, R.R. Effect of various normalization methods on applied biosystems expression array system data. BMC Bioinform. 2006, 7, 533, doi:10.1186/1471-2105-7-533.
[35]  DAVID: Functional Annotation Result Summary. Available online: http://david.abcc.ncifcrf.gov/ (accessed on 13 May 2013).
[36]  Dennis, G., Jr.; Sherman, B.T.; Hosack, D.A.; Yang, J.; Gao, W.; Lane, H.C.; Lempicki, R.A. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 2003, 4, doi:10.1186/gb-2003-4-9-r60.
[37]  Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4, 44–57.
[38]  Swaminathan, S.; Fonseca, V.A.; Alam, M.G.; Shah, S.V. The role of iron in diabetes and its complications. Diabetes Care 2007, 30, 1926–1933, doi:10.2337/dc06-2625.
[39]  Rajpathak, S.N.; Crandall, J.P.; Wylie-Rosett, J.; Kabat, G.C.; Rohan, T.E.; Hu, F.B. The role of iron in type 2 diabetes in humans. Biochim. Biophys. Acta 2009, 1790, 671–681, doi:10.1016/j.bbagen.2008.04.005.
[40]  Cooksey, R.C.; Jouihan, H.A.; Ajioka, R.S.; Hazel, M.W.; Jones, D.L.; Kushner, J.P.; McClain, D.A. Oxidative stress, beta-cell apoptosis, and decreased insulin secretory capacity in mouse models of hemochromatosis. Endocrinology 2004, 145, 5305–5312.
[41]  Huang, J.; Gabrielsen, J.S.; Cooksey, R.C.; Luo, B.; Boros, L.G.; Jones, D.L.; Jouihan, H.A.; Soesanto, Y.; Knecht, L.; Hazel, M.W.; et al. Increased glucose disposal and AMP-dependent kinase signaling in a mouse model of hemochromatosis. J. Biol. Chem. 2007, 282, 37501–37507.
[42]  Viola, A.; Pagano, L.; Laudati, D.; D’Elia, R.; D’Amico, M.R.; Ammirabile, M.; Palmieri, S.; Prossomariti, L.; Ferrara, F. HFE gene mutations in patients with acute leukemia. Leuk Lymphoma 2006, 47, 2331–2334.
[43]  Morey, J.S.; Ryan, J.C.; van Dolah, F.M. Microarray validation: Factors influencing correlation between oligonucleotide microarrays and real-time PCR. Biol. Proced. Online 2006, 8, 175–193, doi:10.1251/bpo126.
[44]  Tefferi, A.; Bolander, M.E.; Ansell, S.M.; Wieben, E.D.; Spelsberg, T.C. Primer on medical genomics. Part III: Microarray experiments and data analysis. Mayo Clin. Proc. 2002, 77, 927–940.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413