Skip to main content Accessibility help
×
Home
Hostname: page-component-78dcdb465f-f64jw Total loading time: 0.438 Render date: 2021-04-20T03:03:21.995Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

Defining best practice for microarray analyses in nutrigenomic studies

Published online by Cambridge University Press:  08 March 2007

Paola Garosi
Affiliation:
Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA, UK
Carlotta De Filippo
Affiliation:
Department of Pharmacology, University of Florence, Florence, Italy
Marjan van Erk
Affiliation:
TNO Nutrition and Food Research, PO Box 360, 3700 AJ, Zeist, The Netherlands
Philippe Rocca-Serra
Affiliation:
EMBL-EBI, The European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK
Susanna-Assunta Sansone
Affiliation:
EMBL-EBI, The European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK
Ruan Elliott
Affiliation:
Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA, UK
Corresponding
E-mail address:
Rights & Permissions[Opens in a new window]

Abstract

Microarrays represent a powerful tool for studies of diet–gene interactions. Their use is, however, associated with a number of technical challenges and potential pitfalls. The cost of microarrays continues to drop but is still comparatively high. This, coupled with the complex logistical issues associated with performing nutritional microarray studies, often means that compromises have to be made in the number and type of samples analysed. Additionally, technical variations between array platforms and analytical procedures will almost inevitably lead to differences in the transcriptional responses observed. Consequently, conflicting data may be produced, important effects may be missed and/or false leads generated (e.g. apparent patterns of differential gene regulation that ultimately prove to be incorrect or not significant). This is likely to be particularly true in the field of nutrition, in which we expect that many dietary bioactive agents at nutritionally relevant concentrations will elicit subtle changes in gene transcription that may be critically important in biological terms but will be difficult to detect reliably. Thus, great care should always be taken in designing and executing microarray studies. This article seeks to provide an overview of both the main practical and theoretical considerations in microarray use that represent potential sources of technical variation and error. Wherever possible, recommendations are made on what we propose to be the best approach. The overall aims are to provide a basic framework of advice for researchers who are new to the use of microarrays and to promote a discussion of standardisation and best practice in the field.

Type
Research Article
Copyright
Copyright © The Nutrition Society 2005

References

Ahmed, AA, Vias, M, Iyer, NG, Caldas, C & Brenton, JD (2004) Microarray segmentation methods significantly influence data precision. Nucleic Acids Res 32, e50.CrossRefGoogle ScholarPubMed
Badiee, A, Eiken, HG, Steen, VS & Lovlie, R (2003) Evaluation of five different cDNA labelling methods for microarrays using spike controls. BMC Biotechnol 3, 23.CrossRefGoogle Scholar
Baker, VA, Harries, HM & Waring, JF (2004) Clofibrate-induced gene expression changes in rat-liver: a cross laboratory analysis using membrane cDNA arrays. Environ Health Perspect 112, 428438.CrossRefGoogle ScholarPubMed
Baugh, LR, Hill, AA, Brown, E & Hunter, CP (2001) Quantitative analysis of mRNA amplification by in vivo transcription. Nucleic Acid Res 29, E29.CrossRefGoogle Scholar
Benes, V & Muckenthaler, M (2003) Standardization of protocols in cDNA microarray analysis. Trends Biochem Sci 28, 244249.CrossRefGoogle ScholarPubMed
Bolstad, BM, Irizarry, RA, Astrand, M & Speed, TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185193.CrossRefGoogle ScholarPubMed
Brazma, A, Hingkamp, P & Quackenbush, J (2001) Minimum information about a microarray experiment (MIAME) – toward standards for microarray data. Nat Genet 29, 365371.CrossRefGoogle ScholarPubMed
Cao, YA, Lee, SY, Kim, JW, Chang, MS & & Choi, S (2003) Cross comparison of DNA microarray platforms. Brief communication on: http://www.signaling-gateway.org/reports/v1/DA0010.pdfGoogle Scholar
Chiang, MK & Melton, DA (2003) Single-cell transcript analysis of pancreas development. Dev Cell 4, 383393.CrossRefGoogle ScholarPubMed
Chu, TM, Deng, S, Wolfinger, R, Paules, RS & Hamadeh, HK (2004) Cross-site comparison of gene expression data reveals high similarity. Environ Health Perspect 112, 4 449455.CrossRefGoogle ScholarPubMed
Cui, X & Churchill, GA (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol 4, 210219.CrossRefGoogle ScholarPubMed
Dahlquist, KD, Salomonis, N, Iranians, K, Lawlor, SC & Conklin, BR (2002) GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 31, 1920.CrossRefGoogle ScholarPubMed
Draghici, S (2002) Statistical intelligence: effective analysis of high-density microarray data. Drug Discov Today 7, 5563Suppl.CrossRefGoogle ScholarPubMed
Duggan, DG, Bittner, M, Chen, Y, Meltzer, P & Trent, JM (1999) Expression profiling using cDNA microarrays. Nat Genet 21, 1014.CrossRefGoogle ScholarPubMed
Efron, B, Tibshirani, R, Storey, JD & Tusher, V (2001) Empirical Bayes analysis of microarray data. J Am Stat Assoc 96, 11511160.CrossRefGoogle Scholar
Forster, T, Roy, D & Ghazal, P (2003) Experiments using microarray technology: limitations and standard operating procedures. J Endocrinol 178, 195204.CrossRefGoogle ScholarPubMed
Grosu, P, Townsend, JP, Hart, DL & Cavalieri, D (2002) Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Res 12, 11211126.CrossRefGoogle ScholarPubMed
Hedge, PQ, Rong, K, Abernathy, C, Gay, S, Dharap, R, Gaspards, J, Earle-Hughes, E & Snerud, E (2002) A concise guide to cDNA microarray analysis. II. Biotechniques 29, 548562.Google Scholar
Hoffmann, R & Valencia, A (2004) A gene network for navigating the literature. Nat Genet 36, 664.CrossRefGoogle ScholarPubMed
Hwa, Yang, Y, Speed T (2002) Design issues for cDNA microarray experiments. Nat Rev Genet 3, 579588.Google Scholar
Ihaka, R & Gentleman, R (1996) A language for data analysis and graphics. J Comput Graph Stat 5, 299314.Google Scholar
Kane, MD, Jatkoe, TA, Stumpf, CR, Lu, J, Thomas, JD & Madore, SJ (2000) Assessment of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acid Res 28, 45524557.CrossRefGoogle ScholarPubMed
Kerr, MK (2003) Design considerations for efficient and effective microarray studies. Biometrics 59, 822828.CrossRefGoogle ScholarPubMed
Kerr, MK & Churchill, GA (2001) Experimental design for gene expression microarrays. Biostatistics 2, 183201.CrossRefGoogle ScholarPubMed
Kerr, K, Martin, M & Churchill, G (2001) Analysis of variance for gene expression microarray data. J Comput Biol 7, 2327.Google Scholar
Kim, H, Zhao, B, Snerud, EC, Haas, BJ, Town, CD & Quackenbush, J (2002) Use of RNA and genomic DNA references for inferred comparison in DNA microarray analyses. Biotechniques 33, 924930.Google Scholar
Kroll, TC & Wolfl, S (2002) Ranking: a closer look on globalisation methods for normalisation of gene expression arrays. Nucleic Acids Res 30 e50.CrossRefGoogle ScholarPubMed
Long, AD, Mangalam, HJ, Chann, BYP, Tolleri, L, Hatfield, GW & Baldi, P (2001) Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. J Biol Chem 276, 1993719944.CrossRefGoogle Scholar
Lyng, H, Badiee, A, Svendsrud, D, Hovig, E, Myklebost, O & Stokke, T (2004) Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction. BMC Genomics 5, 1019.CrossRefGoogle ScholarPubMed
Manduchi, E, Scearce, LM, Brestelli, JE, Grant, GR, Kaestner, KH & Stoeckert, CJ (2002) Comparison of different labelling methods for two-channel high-density microarray experiments. Physiol Genomics 10, 169179.CrossRefGoogle Scholar
Misra, J, Schmitt, W, Hwang, D, Hsiao, LL, Gullans, S & Stephanopoulos, G (2002) Interactive exploration of microarray gene expression patterns in a reduced dimensional space. Genome Res 12, 11121120.CrossRefGoogle Scholar
Naderi, A, Ahmed, AA, Barbosa-Morais, NL, Aparicio, S, Brenton, JD & Caldas, C (2004) Expression microarray reproducibility is improved by optimising purification steps in RNA amplification and labelling. BMC Genomics 5, 922.CrossRefGoogle ScholarPubMed
Nilsen, TW, Grayzel, J & Prensky, W (1997) Dendritic nucleic acid structures. J Theor Biol 187, 273284.CrossRefGoogle ScholarPubMed
Novoradovskaya, N, Whitfield, ML & Basehore, LS (2004) Universal reference RNA as a standard for microarray experiments. BMC Genomics 5, 2032.CrossRefGoogle ScholarPubMed
Pandey, R, Guru, RK & Mount, DW (2004) Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data. Bioinformatics 20, 21562158.CrossRefGoogle ScholarPubMed
Park, CH, Jeong, HJ, Jung, JJ, Lee, GY, Kim, SC, Kim, TS, Yang, SH, Chung, HC & Rha, SY (2004) Fabrication of high quality cDNA microarray using a small amount of cDNA. Int J Mol Med 13, 675679.Google ScholarPubMed
Quackenbush, J (2001) Computational analysis of microarray data. Nat Genet 2, 418427.CrossRefGoogle ScholarPubMed
Quackenbush, J (2002) Microarray normalization and transformation. Nat Genet 32, 496501.CrossRefGoogle Scholar
Reiner, A, Yekutieli, D & Benjamini, Y (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19, 368375.CrossRefGoogle ScholarPubMed
Rickman, DS, Herbert, CJ & Aggerbeck, LP (2003) Optimising solutions for increased reproducibility of cDNA microarrays. Nucleic Acid Res 31 e109.CrossRefGoogle Scholar
Roth, ME, Feng, L & McConnell, KJ (2004) Expression profiling using a hexamer-based universal microarray. Nat Biotechnol 22, 418426.CrossRefGoogle ScholarPubMed
Ryan, MM & Huffaker, SJ (2004) Application and optimisation of microarray technologies for human post-mortem brain studies. Biol Psychiatr 55, 329336.CrossRefGoogle Scholar
Slonim, DK (2002) From patterns to pathways: gene expression data analysis comes of age. Nat Genet 32, 502508.CrossRefGoogle ScholarPubMed
Tan, PK, Downey, TJ & Spitznagel, JR (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acid Res 31, 56765684.CrossRefGoogle ScholarPubMed
Taniguchi, M, Miura, K, Iwao, H & Yamanaka, S (2001) Quantitative assessment of DNA microarrays – comparison with Northern blot analysis. Genomics 71, 3439.CrossRefGoogle Scholar
Taylor, S, Smith, S, Windle, B, Guiseppi-Elie, A (2003) Impact of surface chemistry and blocking strategies on DNA microarrays. Nucleic Acid Res 31 e87.CrossRefGoogle ScholarPubMed
Tolstrup, N, Nielsen, N, Kolberg, JG, Frankel, AM, Vissing, H & Kauppinen, S (2003) OligoDesign: optimal design of LNA (locked nucleic acid) oligonucleotide capture probes for gene expression profiling. Nucleic Acid Res 31, 37583762.CrossRefGoogle ScholarPubMed
Townsend, JP (2003) Multifactorial expression design and transitivity of ratios with spotted DNA microarrays. BMC Genomics 4, 4149.CrossRefGoogle ScholarPubMed
Tzu-Ming, C, Deng, S, Wolfinger, R, Paules, RS & Hamadeh, HK (2004) Cross-site comparison of gene expression data reveals high similarity. Environ Health Perspect 112, 449455.Google Scholar
Ulrich, RG, Rockett, JC, Gibson, GG & Pettit, SD (2004) Overview of an interlaboratory collaboration on evaluating the effects of model hepatotoxicants on hepatic gene expression. Environ Health Perspect 112, 423427.CrossRefGoogle ScholarPubMed
van de, Peppel, J, Kemmeren, P, van, Bakel, H, Radonjic, M, van Leenen, D & Holstege, FC (2003) Monitoring global messenger RNA changes in externally controlled microarray experiments. EMBO Rep 4, 387393.CrossRefGoogle Scholar
Van Gelder, RN, von Zastrow, ME, Yool, A, Dement, WC, Barchas, JD & Eberwine, JH (1990) Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci USA 87, 16631667.CrossRefGoogle ScholarPubMed
Van Hal, FNLW, Vorst, O, Kramer, E, Hall, DR & Keijer, J (2002) Factors influencing cDNA microarray hybridisation on silylated glass slides. Anal Biochem 308, 517.CrossRefGoogle Scholar
Wang, H-Y, Malek, RL, Kwitek, AE, Greene, AS & Luu, TV (2003) Assessing unmodified 70-mer oligonucleotide probe performance on glass slide microarrays. Genome Biol 4 R5.CrossRefGoogle ScholarPubMed
Waring, JF, Ulrich, RG, Flint, N, Morfitt, D, Kalkuhl, A, Staedtler, F, Lawton, M, Beekman, JM & Suter, L (2004) Interlaboratory evaluation of rat hepatic gene expression changes induced by methapyrilene. Environ Health Perspect 112, 439448.CrossRefGoogle ScholarPubMed
Wrobel, G, Schlingemann, J, Hummerich, L, Kramer, H, Lichter, P & Hahn, M (2003) Optimisation of high-density cDNA microarray protocols by ‘design experiments’. Nucleic Acid Res 31 e67.CrossRefGoogle Scholar
Wurmbach, E, Yuen, T & Sealfon, SC (2003) Focused microarray analysis. Methods 31, 306316.CrossRefGoogle ScholarPubMed
Xiang, C, Chen, M, Ma, L, Phan, QN, Inman, JM, Kozhich, OA & Brownstein, MJ (2003) A new strategy to amplify degraded RNA from small tissue samples for microarray studies Nucleic Acids Res e53.Google Scholar
Yang, YH, Buckley, MJ, Dudoit, S & Speed, TP (2001a) Comparison of Methods for Image Analysis on cDNA Microarray Data. Technical report no. 584 Berkley, CA Department of Statistics, University of CaliforniaGoogle Scholar
Yang, HY, Buckley, MJ & Speed, TP (2001b) Analysis of cDNA microarray images. Brief Bioinform 2, 341349.CrossRefGoogle ScholarPubMed
Yang, IV, Chen, E & Hasseman, JP (2002) Within the fold: assessing differential expression measures and reproductability in micro:array assays. Genome Biol 24, 3.Google Scholar
Zhou, Y & Abagyan, R (2002) Match-only Integral Distribution (MOID) algorithm for high-density oligonucleotide array analysis. BMC Bioinformatics 3, 311.CrossRefGoogle ScholarPubMed

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 0
Total number of PDF views: 205 *
View data table for this chart

* Views captured on Cambridge Core between September 2016 - 20th April 2021. This data will be updated every 24 hours.

You have Access

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Defining best practice for microarray analyses in nutrigenomic studies
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Defining best practice for microarray analyses in nutrigenomic studies
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Defining best practice for microarray analyses in nutrigenomic studies
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *