Cited by 1,339Gene expression and cancer classificationBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 97 of the 99 references it cites were found in a registry or an index.

Microarray data analysis: from disarray to consolidation and consensus

Allison, David B., Cui, Xiangqin, Page, Grier P., Sabripour, Mahyar·2006·Nature Reviews Genetics

DOI 10.1038/nrg1749

Open this paper in CoChat — free account

Abstract

In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.

Integrity signals

Registry

Crossref · 10.1038/nrg1749

Cites

97 of 97 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Cluster Analysis
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,186
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,837
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.
Yang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., & Speed, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.4.e15 · Cited by 3,722
The positive false discovery rate: a Bayesian interpretation and the q-value
Storey, J. D. (2003) · The Annals of Statistics · doi:10.1214/aos/1074290335 · Cited by 2,204
Microarray data normalization and transformation
Quackenbush, J. (2002) · Nature Genetics · doi:10.1038/ng1032 · Cited by 1,975

Flagged first, then by citations

Show all 99 references

2 of the 99 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

Osier, M. V. in DNA Microarrays and Statistical Genomic Techniques: Design, Analysis, and Interpretation of Experiments (Marcel Dekker, New York, 2005).
Dobbin, K. & Simon, R. Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 6, 27–38 (2005). · DOI

Sorted by citations

Items 201–300 of 1,000

Identification of differential gene pathways with principal component analysis
Ma, S., & Kosorok, M. R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp085 · Cited by 75
Visualizing Gene-Set Enrichment Results Using the Cytoscape Plug-in Enrichment Map
Merico, D., Isserlin, R., & Bader, G. D. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-276-2_12 · Cited by 76
Identification of differential translation in genome wide studies
Larsson, O. S., Sonenberg, N., & Nadon, R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006821107 · Cited by 72
Approaches to working in high-dimensional data spaces: gene expression microarrays
Wang, Y., Miller, D. J., & Clarke, R. C. (2008) · British Journal of Cancer · doi:10.1038/sj.bjc.6604207 · Cited by 74
Plastic and adaptive gene expression patterns associated with temperature stress in Arabidopsis thaliana
Swindell, W. R., Huebner, M., & Weber, A. P. M. (2007) · Heredity · doi:10.1038/sj.hdy.6800975 · Cited by 74
A statistical framework for testing functional categories in microarray data
Barry, W. T., Nobel, A. B., & Wright, F. A. (2008) · The Annals of Applied Statistics · doi:10.1214/07-aoas146 · Cited by 73
Genome-wide gene expression profiling suggests distinct radiation susceptibilities in sporadic and post-Chernobyl papillary thyroid cancers
Detours, V., Delys, L., Libert, F., Solís, D. W., Ti, B., Dumont, J. E., Franc, B., Thomas, G. A., & Maenhaut, C. (2007) · British Journal of Cancer · doi:10.1038/sj.bjc.6603938 · Cited by 75
Image Defocus and Altered Retinal Gene Expression in Chick: Clues to the Pathogenesis of Ametropia
Stone, R. A., McGlinn, A. M., Baldwin, D. A., Tobias, J. W., Iuvone, P. M., & Khurana, T. S. (2011) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.10-6727 · Cited by 72
Positional distribution of human transcription factor binding sites
Koudritsky, M., & Domany, E. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn752 · Cited by 71
Leukocyte subset-derived genomewide expression profiles in pediatric septic shock*
Wong, H. R., Freishtat, R. J., Monaco, M., Odoms, K., & Shanley, T. P. (2009) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0b013e3181c519b4 · Cited by 72
Circulating IGF1 regulates hippocampal IGF1 levels and brain gene expression during adolescence
Han, Y., Mitschelen, M. C., Bixler, G. V., Brucklacher, R. M., Farley, J. A., Han, S. I., Freeman, W. M., & Sonntag, W. E. (2011) · Journal of Endocrinology · doi:10.1530/joe-11-0200 · Cited by 72
A methodology for global validation of microarray experiments
Miron, M., Woody, O. Z., Marcil, A., Murie, C., SLADEK, R. M., & Nadon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-333 · Cited by 72
Microarray challenges in ecology.
Kammenga, J., Herman, M. A., Ouborg, N., Johnson, L., & Breitling, R. (2007) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2007.01.013 · Cited by 79
Heading Down the Wrong Pathway: on the Influence of Correlation within Gene Sets
Gatti, D. M., Barry, W. T., Nobel, A. B., Rusyn, I., & Wright, F. A. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-574 · Cited by 72
Clinical review: Sepsis and septic shock - the potential of gene arrays
Wong, H. R. (2012) · Critical Care · doi:10.1186/cc10537 · Cited by 72
Genetics of abdominal aortic aneurysm
Golledge, J., & Kuivaniemi, H. (2013) · Current Opinion in Cardiology · doi:10.1097/hco.0b013e32835f0d55 · Cited by 72
Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
Hopfensitz, M., Müssel, C., Wawra, C., Maucher, M., Kühl, M., Heiko Neumann, B., & Kestler, H. A. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.62 · Cited by 69
A hybrid LDA and genetic algorithm for gene selection and classification of microarray data
Huerta, E. B., Duval, B., & Hao, J. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.03.024 · Cited by 70
Gender differences in white matter pathology and mitochondrial dysfunction in Alzheimer’s disease with cerebrovascular disease
Gallart‐Palau, X., Lee, B. S. T., Adav, S. S., Qian, J., Serra, A., Park, J. E., Lai, M. K. P., Chen, C., Kalaria, R. N., & Sze, S. K. (2016) · Molecular Brain · doi:10.1186/s13041-016-0205-7 · Cited by 72
Transcriptomics: mRNA and alternative splicing
Sánchez‐Pla, Á., Reverter, F., de Villa, M. C. R., & Comabella, M. (2012) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2012.04.008 · Cited by 69
Expectations, validity, and reality in gene expression profiling
Kim, K., Zakharkin, S. O., & Allison, D. B. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.02.018 · Cited by 69
Self-citation
Modeling, inference and optimization of regulatory networks based on time series data
Weber, G., Defterli, Ö., Gök, S. Z. A., & Kropat, E. (2010) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.06.038 · Cited by 69
Transcriptional responses to complex mixtures—A review
Sen, B., Mahadevan, B., & DeMarini, D. M. (2007) · Mutation Research/Reviews in Mutation Research · doi:10.1016/j.mrrev.2007.08.002 · Cited by 70
GEPAS, a web-based tool for microarray data analysis and interpretation
Tárraga, J., Medina, I., Carbonell‐Caballero, J., Huerta‐Cepas, J., Mínguez, P., Alloza, E., Al‐Shahrour, F., Vegas-Azcárate, S., Götz, S., Escobar, P., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn303 · Cited by 70
Self-Contained Gene-Set Analysis of Expression Data: An Evaluation of Existing and Novel Methods
Fridley, B. L., Jenkins, G. D., & Biernacka, J. M. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012693 · Cited by 68
Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site
Tabach, Y., Brosh, R., Buganim, Y., Benaim, A. R., Zuk, O., Yitzhaky, A., Koudritsky, M., Rotter, V., & Domany, E. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000807 · Cited by 67
Reproducibility-Optimized Test Statistic for Ranking Genes in Microarray Studies
Elo, L. L., Filén, S., Lahesmaa, R., & Aittokallio, T. A. (2008) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.1078 · Cited by 68
Integrated analysis of microRNA and mRNA expression: adding biological significance to microRNA target predictions
van Iterson, M., Bervoets, S., de Meijer, E. J., Buermans, H. P. J., Hoen, P. A. C. ’., de Menezes, R. X., & Boer, J. M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt525 · Cited by 67
Microarray Analysis of miRNA Gene Expression
Thomson, J. M., Parker, J. S., & Hammond, S. M. (2007) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(07)27006-5 · Cited by 68
Statistical Model to Analyze Quantitative Proteomics Data Obtained by 18O/16O Labeling and Linear Ion Trap Mass Spectrometry
Jorge, I., Navarro, P., Martínez-Acedo, P., Núñez, E., Serrano, H., Alfranca, A., Redondo, J. M., & Vázquez, J. (2009) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800260-mcp200 · Cited by 67
Early PLDα-mediated events in response to progressive drought stress in Arabidopsis: a transcriptome analysis
Mane, S. P., Vásquez-Robinet, C., Sioson, A. A., Heath, L. S., & Grene, R. (2007) · Journal of Experimental Botany · doi:10.1093/jxb/erl262 · Cited by 67
Advances in metaheuristics for gene selection and classification of microarray data
Duval, B., & Hao, J. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp035 · Cited by 67
Effects of Tobacco Smoke on Gene Expression and Cellular Pathways in a Cellular Model of Oral Leukoplakia
Gumus, Z. H., Du, B., Kacker, A., Boyle, J. O., Bocker, J. M., Mukherjee, P., Subbaramaiah, K., Dannenberg, A. J., & Weinstein, H. (2008) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-08-0007 · Cited by 67
Data Science in Radiology: A Path Forward
Aerts, H. J. W. L. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2804 · Cited by 70
Sulphur dioxide evokes a large scale reprogramming of the grape berry transcriptome associated with oxidative signalling and biotic defence responses
Giraud, E., Ivanova, A., GORDON, C. S., Whelan, J., & Considine, M. J. (2011) · Plant Cell & Environment · doi:10.1111/j.1365-3040.2011.02379.x · Cited by 66
Gene expression changes in the medial prefrontal cortex and nucleus accumbens following abstinence from cocaine self-administration
Freeman, W. M., Lull, M. E., Patel, K. M., Brucklacher, R. M., Morgan, D., Roberts, D. C. S., & Vrana, K. E. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-29 · Cited by 70
Gene differential coexpression analysis based on biweight correlation and maximum clique
Zheng, C.-H., Yuan, L., Sha, W., & Sun, Z.-L. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s15-s3 · Cited by 65
Mining SOM expression portraits: Feature selection and integrating concepts of molecular functionPreprint
Loeffler‐Wirth, H., von Bergen�, M., & Binder, H. (2011) · Nature Precedings · doi:10.1186/1756-0381-5-18 · Cited by 65
Downsizing genomic medicine: Approaching the ethical complexity of whole-genome sequencing by starting small
Sharp, R. R. (2011) · Genetics in Medicine · doi:10.1097/gim.0b013e31820f603f · Cited by 64
LEVERAGING BIOLOGICAL REPLICATES TO IMPROVE ANALYSIS IN CHIP-SEQ EXPERIMENTS
Yang, Y., Fear, J. M., Hu, J., Häecker, I., Zhou, L. J., Renne, R., Bloom, D. C., & McIntyre, L. M. (2014) · Computational and Structural Biotechnology Journal · doi:10.5936/csbj.201401002 · Cited by 63
Molecular and whole animal responses of grass shrimp, Palaemonetes pugio, exposed to chronic hypoxia
Brouwer, M. H., Brown‐Peterson, N. J., Larkin, P. D., Patel, V., Denslow, N. D., Manning, S., & Brouwer, T. H. (2006) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2006.10.049 · Cited by 65
A Discussion of Statistical Methods for Design and Analysis of Microarray Experiments for Plant Scientists
Nettleton, D. S. (2006) · The Plant Cell · doi:10.1105/tpc.106.041616 · Cited by 65
Chromosome-wide identification of novel imprinted genes using microarrays and uniparental disomies
Schulz, R. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl461 · Cited by 65
Systems interface biology
Doyle, F. J., & Stelling, J. (2006) · Journal of The Royal Society Interface · doi:10.1098/rsif.2006.0143 · Cited by 60
mi RNA expression profile of retinal pigment epithelial cells under oxidative stress conditions
Donato, L., Bramanti, P., Scimone, C., Rinaldi, C., D’Angelo, R., & Sidoti, A. (2017) · FEBS Open Bio · doi:10.1002/2211-5463.12360 · Cited by 66
Chronic quercetin exposure affects fatty acid catabolism in rat lung
de Boer, V. C. J., van Schothorst, E. M., Dihal, A. A., van der Woude, H., Arts, I. C. W., Rietjens, I. M. C. M., Hollman, P. C. H., & Keijer, J. (2006) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-006-6316-z · Cited by 64
Gene expression in the liver of rainbow trout, Oncorhynchus mykiss, during the stress response
Momoda, T. S., Schwindt, A. R., Feist, G. W., Gerwick, L. G., Bayne, C. J., & Schreck, C. B. (2007) · Comparative Biochemistry and Physiology Part D Genomics and Proteomics · doi:10.1016/j.cbd.2007.06.002 · Cited by 70
COPD subtypes identified by network-based clustering of blood gene expression
Chang, Y., Glass, K., Liu, Y., Silverman, E. K., Crapo, J. D., Tal‐Singer, R., Bowler, R. P., Dy, J., Cho, M., & Castaldi, P. J. (2016) · Genomics · doi:10.1016/j.ygeno.2016.01.004 · Cited by 64
Isolation of Human Embryonic Stem Cell–Derived Teratomas for the Assessment of Pluripotency
Gertow, K., Przyborski, S. A., Loring, J. F., Auerbach, J. M., Epifano, O., Otonkoski, T., Damjanov, I., & Ährlund‐Richter, L. (2007) · Current Protocols in Stem Cell Biology · doi:10.1002/9780470151808.sc01b04s3 · Cited by 63
Impaired barrier function by dietary fructo-oligosaccharides (FOS) in rats is accompanied by increased colonic mitochondrial gene expression
Rodenburg, W., Keijer, J., Kramer, E., Vink, C., van der Meer, R., & Bovee-Oudenhoven, I. M. J. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-144 · Cited by 62
An Integrated Approach for the Analysis of Biological Pathways using Mixed Models
Wang, L., Zhang, B., Wolfinger, R. D., & Chen, X. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000115 · Cited by 62
Expression of mitochondrial dysfunction-related genes and pathways in paclitaxel-induced peripheral neuropathy in breast cancer survivors
Kober, K. M., Olshen, A. B., Conley, Y. P., Schumacher, M. A., Topp, K. S., Smoot, B. J., Mazor, M. B., Chesney, M. A., Hammer, M. J., Paul, S. M., et al. (2018) · Molecular Pain · doi:10.1177/1744806918816462 · Cited by 66
Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization
Elyasigomari, V., Mirjafari, M. S., Screen, H. R. C., & Shaheed, M. H. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.06.015 · Cited by 62
Atherosclerosis
Miller, D. T., Ridker, P. M., Libby, P., & Kwiatkowski, D. J. (2007) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2006.12.045 · Cited by 64
Slc27a2 expression in peripheral blood mononuclear cells as a molecular marker for overweight development
Caimari, A., Oliver, P., Rodenburg, W., Keijer, J., & Palou, A. (2010) · International Journal of Obesity · doi:10.1038/ijo.2010.17 · Cited by 61
Clustering of genes into regulons using integrated modeling-COGRIM
Chen, G., Jensen, S. T., & Stoeckert, C. J. (2007) · Genome biology · doi:10.1186/gb-2007-8-1-r4 · Cited by 61
Statistical methods for identifying differentially expressed genes in RNA-Seq experiments
Fang, Z., Martin, J. V., & Wang, Z. (2012) · Cell & Bioscience · doi:10.1186/2045-3701-2-26 · Cited by 61
Radiofrequency Radiation and Gene/Protein Expression: A Review
McNamee, J. P., & Chauhan, V. (2009) · Radiation Research · doi:10.1667/rr1726.1 · Cited by 61
Sexual differentiation of the zebra finch song system: potential roles for sex chromosome genes
Tomaszycki, M. L., Peabody, C., Replogle, K. L., Clayton, D. F., Tempelman, R., & Wade, J. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-24 · Cited by 69
Review and Cross-Validation of Gene Expression Signatures and Melanoma Prognosis
Schramm, S., Campain, A. E., Yang, J. Y. H., & Mann, G. J. (2011) · Journal of Investigative Dermatology · doi:10.1038/jid.2011.305 · Cited by 60
Green tea extract reduces induction of p53 and apoptosis in UVB‐irradiated human skin independent of transcriptional controls
Mnich, C. D., Hoek, K. S., Virkki, L. V., Farkas, Á., Dudli, C., Laine, E., Urosevic, M., & Dummer, R. (2008) · Experimental Dermatology · doi:10.1111/j.1600-0625.2008.00765.x · Cited by 61
Gene Expression Profiles of the Spleen, Liver, and Head Kidney in Turbot (Scophthalmus maximus) Along the Infection Process with Aeromonas salmonicida Using an Immune-Enriched Oligo-microarray
Millán, A., Gómez‐Tato, A., Pardo, B. G., Fernández, C., Bouza, C., Vera, M., Álvarez‐Dios, J. A., Cabaleiro, S., Lamas, J., Lemos, M. L., et al. (2011) · Marine Biotechnology · doi:10.1007/s10126-011-9374-7 · Cited by 59
Gene profiling in experimental models of eye growth: Clues to myopia pathogenesis
Stone, R. A., & Khurana, T. S. (2010) · Vision Research · doi:10.1016/j.visres.2010.03.021 · Cited by 59
Preprocessing and downstream analysis of microarray DNA copy number profiles
van de Wiel, M. A., Picard, F., van Wieringen, W. N., & Ylstra, B. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbq004 · Cited by 59
Meta-analysis of glioblastoma multiforme versus anaplastic astrocytoma identifies robust gene markers
Dreyfuss, J. M., Johnson, M. D., & Park, P. J. (2009) · Molecular Cancer · doi:10.1186/1476-4598-8-71 · Cited by 58
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
Freyhult, E., Landfors, M., Önskog, J., Hvidsten, T. R., & Rydén, P. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-503 · Cited by 59
Effects of different leaf litters on the physicochemical properties and bacterial communities in Panax ginseng -growing soil
Sun, H. F., Wang, Q., Liu, N., Li, L., Zhang, C.-G., Liu, Z.-B., & Zhang, Y. (2016) · Applied Soil Ecology · doi:10.1016/j.apsoil.2016.11.008 · Cited by 58
Genome Wide Gene Expression Studies in Mood Disorders
Sequeira, A., & Turecki, G. (2006) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2006.10.444 · Cited by 59
A comparison of analog and Next-Generation transcriptomic tools for mammalian studies
Roy, N. C., Altermann, E., Park, Z. A., & McNabb, W. C. (2011) · Briefings in Functional Genomics · doi:10.1093/bfgp/elr005 · Cited by 58
A Perspective on DNA Microarrays in Pathology Research and Practice
Pollack, J. R. (2007) · American Journal Of Pathology · doi:10.2353/ajpath.2007.070342 · Cited by 58
Comments on the analysis of unbalanced microarray data
Kerr, K. F. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp363 · Cited by 57
Microarray probes and probe sets
Liu, H. (2010) · Frontiers in Bioscience-Elite · doi:10.2741/e93 · Cited by 57
Incorporating collaboratory concepts into informatics in support of translational interdisciplinary biomedical research
LEE, E., MCDONALD, D., Anderson, N., & Tarczy-Hornoch, P. Z. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2008.06.011 · Cited by 57
BeadArray Expression Analysis Using Bioconductor
Ritchie, M. E., Dunning, M., Smith, M. L., Shi, W., & Lynch, A. G. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002276 · Cited by 56
Comparison of the effects of five dietary fibers on mucosal transcriptional profiles, and luminal microbiota composition and SCFA concentrations in murine colon
Lange, K., Hugenholtz, F., Jonathan, M. C., Schols, H. A., Kleerebezem, M., Smidt, H., Müller, M. M., & Hooiveld, G. J. E. J. (2015) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201400597 · Cited by 56
virtualArray: a R/bioconductor package to merge raw data from different microarray platforms
Heider, A., & Alt, R. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-75 · Cited by 58
Sources of variation in false discovery rate estimation include sample size, correlation, and inherent differences between groups
Zhang, J., & Coombes, K. R. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s13-s1 · Cited by 56
Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data
Federico, A., Serra, A., Ha, M. K., Kohonen, P. J., Choi, J., Liampa, I., Nymark, P. E. H., Sanabria, N. M., Cattelani, L., Fratello, M., et al. (2020) · Nanomaterials · doi:10.3390/nano10050903 · Cited by 58
Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways
Wu, M. C., & Lin, X. (2009) · Statistical Methods in Medical Research · doi:10.1177/0962280209351925 · Cited by 56
An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions
Miller, D. J., Zhang, Y., Yu, G., Liu, Y., Chen, L., Langefeld, C. D., Herrington, D. M., & Wang, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp435 · Cited by 55
Oxidized Low-Density Lipoprotein Correlates Positively With Toll-Like Receptor 2 and Interferon Regulatory Factor-1 and Inversely With Superoxide Dismutase-1 Expression
Holvoet, P., Davey, P. C., Keyzer, D. D., Doukouré, M., Deridder, E., Bochaton‐Piallat, M., Gabbiani, G., Beaufort, E., Bishay, K., Andrieux, N., et al. (2006) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.0000226553.01555.02 · Cited by 55
Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16
Tintle, N., Borchers, B., Brown, M., & Bekmetjev, A. (2009) · BMC Proceedings · doi:10.1186/1753-6561-3-s7-s96 · Cited by 54
Genetic variations in regulatory pathways of fatty acid and glucose metabolism are associated with obesity phenotypes: a population-based cohort study
van den Berg, S. W., Dollé, M. E. T., Imholz, S., van der A, D. L., Slot, R. V. ‘., Wijmenga, C., Verschuren, W. M. M., Strien, C., Siezen, C. L. E., Hoebee, B., et al. (2009) · International Journal of Obesity · doi:10.1038/ijo.2009.152 · Cited by 53
Protein patterning by microcontact printing using pyramidal PDMS stamps
Filipponi, L., Livingston, P., Kašpar, O., Tokárová, V., & Nicolau, D. V. (2016) · Biomedical Microdevices · doi:10.1007/s10544-016-0036-4 · Cited by 54
Gene Expression Profiles in Fathead Minnow Exposed to 2,4-DNT: Correlation with Toxicity in Mammals
Henri Wintz, A., Yoo, L. J., Loguinov, A. V., Wu, Y., Steevens, J. A., Holland, R. D., Beger, R. D., Perkins, E. J., Hughes, O., & Vulpe, C. D. (2006) · Toxicological Sciences · doi:10.1093/toxsci/kfl080 · Cited by 54
Gene expression changes following extinction testing in a heroin behavioral incubation model
Kuntz-Melcavage, K. L., Brucklacher, R. M., Grigson, P. S., Freeman, W. M., & Vrana, K. E. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-95 · Cited by 53
Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring
Frost, H. R. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa582 · Cited by 52
Modeling the Diagnostic Criteria for Alcohol Dependence with Genetic Animal Models
Crabbe, J. C., Kendler, K. S., & HITZEMANN, R. J. (2011) · Current topics in behavioral neurosciences · doi:10.1007/7854_2011_162 · Cited by 52
Gene expression analysis of induced pluripotent stem cells from aneuploid chromosomal syndromes
Zhang, R., Hao, L., Wang, L., Chen, M., Li, W., Li, R., Yu, J. M., Xiao, J., & Wu, J. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s5-s8 · Cited by 53
A Topology-Based Score for Pathway Enrichment
Ibrahim, M. A.-H., Jassim, S., Cawthorne, M. A., & Langlands, K. (2012) · Journal of Computational Biology · doi:10.1089/cmb.2011.0182 · Cited by 53
Multi-Omics Analysis Provides Novel Insight into Immuno-Physiological Pathways and Development of Thermal Resistance in Rainbow Trout Exposed to Acute Thermal Stress
Roh, H., Kim, A., Kim, N., Lee, Y., & Kim, D. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21239198 · Cited by 54
Gene and protein expression in the myometrium in pregnancy and labor
Breuiller-Fouché, M., & Germain, G. (2006) · Reproduction · doi:10.1530/rep.1.00725 · Cited by 55
Consensus guidelines for microarray gene expression analyses in leukemia from three European leukemia networks
Staal, F. J. T., Cario, G., Cazzaniga, G., Haferlach, T., Heuser, M., Hofmann, W. ‐., Mills, K., Schrappe, M., Stanulla, M., Wingen, L. U., et al. (2006) · Leukemia · doi:10.1038/sj.leu.2404274 · Cited by 53
Microarrays
Plomin, R. J., & Schalkwyk, L. C. (2006) · Developmental Science · doi:10.1111/j.1467-7687.2007.00558.x · Cited by 52
T‐cell activation genes differentially expressed at birth in CD4+T‐cells from children who develop IgE food allergy
Martino, D., Bosco, A., McKenna, K., Hollams, E. M., Mok, D., Holt, P. G., & Prescott, S. L. (2011) · Allergy · doi:10.1111/j.1398-9995.2011.02737.x · Cited by 52
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group
Winter, S. S., Jiang, Z., Khawaja, H. M., Griffin, T. C., Devidas, M., Asselin, B. L., & Larson, R. A. (2007) · Blood · doi:10.1182/blood-2006-12-059790 · Cited by 52
Application of iTRAQ to catalogue the skeletal muscle proteome in pigs and assessment of effects of gender and diet dephytinization
Hakimov, H. A., Walters, S. A., Wright, T. C., Meidinger, R. G., Verschoor, C. P., Gadish, M. A., Chiu, D., Strömvik, M. V., Forsberg, C. W., & Golovan, S. P. (2009) · PROTEOMICS · doi:10.1002/pmic.200900049 · Cited by 53
S-adenosylmethionine modifies cocaine-induced DNA methylation and increases locomotor sensitization in mice
Anier, K., Zharkovsky, A., & Kalda, A. (2013) · The International Journal of Neuropsychopharmacology · doi:10.1017/s1461145713000394 · Cited by 52
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes
Rodenburg, W., Heidema, A. G., Boer, J. M. A., Bovee-Oudenhoven, I. M. J., Feskens, E. J. M., Mariman, E. C. M., & Keijer, J. (2007) · Physiological Genomics · doi:10.1152/physiolgenomics.00167.2007 · Cited by 52
Bioinformatics Genes and Pathway Analysis for Chronic Neuropathic Pain after Spinal Cord Injury
Zhang, G., & Yang, P. (2017) · BioMed Research International · doi:10.1155/2017/6423021 · Cited by 52

Authors: Allison, D. B., Cui, X., Page, G. P., & Sabripour, M.
Affiliations: University of Alabama (US)
Corresponding author: Allison, David B.
ORCID: Allison, David B.
Cui, Xiangqin
Page, Grier P.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2006-01-01
Volume: 7
Issue: 1
Pages: 55-65
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/nrg1749
OpenAlex: W2021129827
Semantic Scholar: 7aeace2306487c5e5f3ff6711e1a5d3fc1e4c899
Cited by: 1,339
References: 99
Citations by year: 2026: 9 · 2025: 10 · 2024: 10 · 2023: 13 · 2022: 19 · 2021: 23 · 2020: 35 · 2019: 35 · 2018: 43 · 2017: 56 · 2016: 48 · 2015: 68 · 2014: 73 · 2013: 69 · 2012: 104
FWCI: 9.79
Type: journal article
Language: en
Update notices: erratum (2006-05-01)
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation
U.S. Department of Defense
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: microarray data analysis, gene expression analysis, statistical analysis, normalization, differential gene expression, consensus methods, reproducibility
MeSH terms: Algorithms, Computer Simulation, Data Interpretation, Statistical, Gene Expression Regulation, Genetic Techniques, Genetics, Humans, Models, Biological, RNA, Messenger, Models, Statistical, Cluster Analysis, DNA, Complementary, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Microarray Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Microarray data analysis: from disarray to consolidation and consensus

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks