Relationship between gene co-expression and probe localization on microarray slides
Open accessAbstract
BACKGROUND: Microarray technology allows simultaneous measurement of thousands of genes in a single experiment. This is a potentially useful tool for evaluating co-expression of genes and extraction of useful functional and chromosomal structural information about genes. RESULTS: In this work we studied the association between the co-expression of genes, their location on the chromosome and their location on the microarray slides by analyzing a number of eukaryotic expression datasets, derived from the S. cerevisiae, C. elegans, and D. melanogaster. We find that in several different yeast microarray experiments the distribution of the number of gene pairs with correlated expression profiles as a function of chromosomal spacing is peaked at short separations and has two superimposed periodicities. The longer periodicity has a spacing of 22 genes (approximately 42 Kb), and the shorter periodicity is 2 genes (approximately 4 Kb). CONCLUSION: The relative positioning of DNA probes on microarray slides and source plates introduces subtle but significant correlations between pairs of genes. Careful consideration of this spatial artifact is important for analysis of microarray expression data. It is particularly relevant to recent microarray analyses that suggest that co-expressed genes cluster along chromosomes or are spaced by multiples of a fixed number of genes along the chromosome.
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Positional effects (edge effects, row/column dependencies, or gradient artifacts) are a persistent factor in multi-well assays and microarray experiments.
“First, positional effects (edge effects, row/column dependencies, or gradient artifacts) are a persistent factor in multi-well assays and microarray experiments30,31,52.”
From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [52]
The Discussion and Conclusions reports spatial artifacts affecting measured co-expression and says the artifact is present in a wide variety of microarray experiments. The Results also describes a systematic artifact linked to probe positions across microarray blocks. Our copy does not address multi-well assays or establish the listed edge, row/column, and gradient effects as a group. (LLM, conf=0.86)
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Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 12,617 · see citing works
Exploring the metabolic and genetic control of gene expression on a genomic scale.
Derisi, J., Iyer, V., & Brown, P. (1997) · Science · doi:10.1126/science.278.5338.680 · Cited by 4,832 · see citing works
Spellman, P. T., Sherlock, G. J., Zhang, M. Q., Iyer, V. R., Anders, K. R., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,724 · see citing works
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 · see citing works
Functional Discovery via a Compendium of Expression Profiles
Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R. B., Armour, C. D., Bennett, H. A., Coffey, E. M., Dai, H. A., He, Y. D., et al. (2000) · Cell · doi:10.1016/s0092-8674(00)00015-5 · Cited by 2,534 · see citing works
Exploring the new world of the genome with DNA microarrays
Brown, P. O., & Botstein, D. (1999) · Nature Genetics · doi:10.1038/4462 · Cited by 2,430 · see citing works
Chromosome territories, nuclear architecture and gene regulation in mammalian cells
Cremer, T. J., & Cremer, C. G. (2001) · Nature Reviews Genetics · doi:10.1038/35066075 · Cited by 2,339 · see citing works
A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle
Cho, R. J., Campbell, M. J., Winzeler, E. A., Steinmetz, L. M., Conway, A. R., Wodicka, L. M., Wolfsberg, T. G., Gabrielian, A. E., Landsman, D., Lockhart, D. J., et al. (1998) · Molecular Cell · doi:10.1016/s1097-2765(00)80114-8 · Cited by 2,024 · see citing works
Genetic diversity and disease control in rice
Zhu, Y., Chen, H., Fan, J., Wang, Y., Li, Y., Chen, J., Fan, J., Yang, S., Hu, L., Leung, H., et al. (2000) · Nature · doi:10.1038/35021046 · Cited by 1,622 · see citing works
A proteomic view of the Plasmodium falciparum life cycle
Florens, L., Washburn, M., Raine, J., Anthony, R. M., Grainger, M., Haynes, J., Moch, J. K., Muster, N., Sacci, J., Tabb, D. L., et al. (2002) · Nature · doi:10.1038/nature01107 · Cited by 1,330 · see citing works
Courcelle, J., Khodursky, A., Peter, B., Brown, P. O., & Hanawalt, P. C. (2001) · Genetics · doi:10.1093/genetics/158.1.41 · Cited by 853 · see citing works
Cohen, B. A., Mitra, R. D., Hughes, J. D., & Church, G. M. (2000) · Nature Genetics · doi:10.1038/79896 · Cited by 533 · see citing works
Evidence for large domains of similarly expressed genes in the Drosophila genome
Spellman, P. T., & Rubin, G. M. (2002) · Journal of Biology · doi:10.1186/1475-4924-1-5 · Cited by 485 · see citing works
Chromosomal clustering of muscle-expressed genes in Caenorhabditis elegans
Roy, P. J., Stuart, J. M., Lund, J., & Kim, S. K. (2002) · Nature · doi:10.1038/nature01012 · Cited by 439 · see citing works
Two yeast forkhead genes regulate the cell cycle and pseudohyphal growth
Zhu, G., Spellman, P. T., Volpe, T., Brown, P. O., Botstein, D., Davis, T. N., & Futcher, B. (2000) · Nature · doi:10.1038/35017581 · Cited by 362 · see citing works
A view of Interphase Chromosomes
Manuelidis, L. (1990) · Science · doi:10.1126/science.2274784 · Cited by 334 · see citing works
RNA polymerase II elongation through chromatin
Orphanides, G., & Reinberg, D. (2000) · Nature · doi:10.1038/35035000 · Cited by 244 · see citing works
Khodursky, A. B., Peter, B. J., Cozzarelli, N. R., Botstein, D., Brown, P. O., & Yanofsky, C. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.220414297 · Cited by 208 · see citing works
Identification and correction of spurious spatial correlations in microarray data
Qian, J. X., Kluger, Y., Yu, H., & Gerstein, M. (2003) · BioTechniques · doi:10.2144/03351bm03 · Cited by 27 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
The model organism as a system: integrating 'omics' data sets
Joyce, A. R., & Palsson, B. Ø. (2006) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1857 · Cited by 790 · see citing works
Data-analysis strategies for image-based cell profiling
Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4397 · Cited by 739 · see citing works
Spatial patterns of transcriptional activity in the chromosome of Escherichia coli
Jeong, K. S., Ahn, J.-Y., & Khodursky, A. B. (2004) · Genome biology · doi:10.1186/gb-2004-5-11-r86 · Cited by 163 · see citing works
Quality Assessment for Short Oligonucleotide Microarray Data
Brettschneider, J. A., Collin, F., Bolstad, B. M., & Speed, T. P. (2008) · Technometrics · doi:10.1198/004017008000000334 · Cited by 131 · see citing works
Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping
Royce, T. E., Rozowsky, J. S., Bertone, P., Samanta, M. P., Štolc, V., Weissman, S. M., Snyder, M. P., & Gerstein, M. (2005) · Trends in Genetics · doi:10.1016/j.tig.2005.06.007 · Cited by 99 · see citing works
Self-citationValidation of alternative methods of data normalization in gene co-expression studies
Reverter, A., Barris, W., McWilliam, S. M., Byrne, K. A., Wang, Y. H., Tan, S. H., Hudson, N. J., & Dalrymple, B. P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti124 · Cited by 67 · see citing works
Zhu, D., Hero, A. O., Qin, Z. S., & Swaroop, A. (2005) · Journal of Computational Biology · doi:10.1089/cmb.2005.12.1029 · Cited by 61 · see citing works
Raghava, G. P. S., & Han, J. H. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-59 · Cited by 60 · see citing works
Wang, Y. C., Juric, D., Francisco, B. S., Yu, R., Durán, G. E., Chen, K. G., Chen, X., & Sikic, B. I. (2005) · Genes Chromosomes and Cancer · doi:10.1002/gcc.20300 · Cited by 56 · see citing works
Sequence biases in large scale gene expression profiling data
Siddiqui, A. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl404 · Cited by 55 · see citing works
Bioinformatics and cancer target discovery
Desany, B. A., & Zhang, Z. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03224-6 · Cited by 53 · see citing works
A non-random walk through the genome.
Oliver, B., & Misteli, T. (2005) · Genome biology · doi:10.1186/gb-2005-6-4-214 · Cited by 47 · see citing works
Widespread promoter-mediated coordination of transcription and mRNA degradation
Dori-Bachash, M., Shalem, O., Manor, Y. S., Pilpel, Y., & Tirosh, I. (2012) · Genome biology · doi:10.1186/gb-2012-13-12-r114 · Cited by 45 · see citing works
Chromosomal clustering of a human transcriptome reveals regulatory background
Vogel, J. H., von Heydebreck, A., Purmann, A., & Sperling, S. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-230 · Cited by 38 · see citing works
Coordinated evolution of co-expressed gene clusters in the Drosophila transcriptome
Mezey, J. G., Nuzhdin, S. V., Ye, F., & Jones, C. D. (2008) · BMC Evolutionary Biology · doi:10.1186/1471-2148-8-2 · Cited by 38 · see citing works
Stokes, T. H., Moffitt, A. B., Phan, J. H., & Wang, M. D. (2007) · Annals of Biomedical Engineering · doi:10.1007/s10439-007-9313-y · Cited by 34 · see citing works
Viñuelas, J., Calevro, F., Rémond, D., Bernillon, J., Rahbé, Y., Febvay, G., Fayard, J., & Charles, H. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-143 · Cited by 31 · see citing works
Hersh, C. P., Pillai, S. G., Zhu, G., Lomas, D. A., Bakke, P. S., Gulsvik, A., DeMeo, D. L., Klanderman, B. J., Lazarus, R. M., Litonjua, A. A., et al. (2010) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200910-1586oc · Cited by 30 · see citing works
Solution structure and dynamics of S100A5 in the apo and Ca2+-bound states
Bertini, I., Gupta, S. D., Hu, X., Karavelas, T., Luchinat, C., Parigi, G., & Yuan, J. (2009) · JBIC Journal of Biological Inorganic Chemistry · doi:10.1007/s00775-009-0553-1 · Cited by 26 · see citing works
Autocorrelation analysis reveals widespread spatial biases in microarray experiments
Koren, A., Tirosh, I., & Barkai, N. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-164 · Cited by 23 · see citing works
Improved Detection of Differentially Expressed Genes Through Incorporation of Gene Locations
Xiao, G., Reilly, C., & Khodursky, A. B. (2009) · Biometrics · doi:10.1111/j.1541-0420.2008.01161.x · Cited by 20 · see citing works
A statistical framework for high-content phenotypic profiling using cellular feature distributions
Pearson, Y. E., Kremb, S., Butterfoss, G. L., Xie, X., Fahs, H., & Gunsalus, K. C. (2022) · Communications Biology · doi:10.1038/s42003-022-04343-3 · Cited by 19 · see citing works
“First, positional effects (edge effects, row/column dependencies, or gradient artifacts) are a persistent factor in multi-well assays and microarray experiments 30,31,52 .”
Interspecies Translation of Disease Networks Increases Robustness and Predictive Accuracy
Anvar, S. Y., Tucker, A., Vinciotti, V., Venema, A., van Ommen, G. B., van der Maarel, S. M., Raz, V., & Hoen, P. A. C. ’. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002258 · Cited by 18 · see citing works
Royce, T. E., Rozowsky, J. S., Luscombe, N. M., Emanuelsson, O., Yu, H., Zhu, X., Snyder, M. P., & Gerstein, M. (2006) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(06)11015-0 · Cited by 16 · see citing works
Self-citationYu, H., Nguyen, K., Royce, T., Qian, J., Nelson, K., Snyder, M., & Gerstein, M. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl871 · Cited by 13 · see citing works
Self-citationBayesian Hierarchical Model for Large-Scale Covariance Matrix Estimation
Zhu, D., & Hero, A. O. (2007) · Journal of Computational Biology · doi:10.1089/cmb.2006.0151 · Cited by 13 · see citing works
Applications of integrative OMICs approaches to gene regulation studies
Qin, J., Yan, B., Hu, Y., Wang, P., & Wang, J. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0085-y · Cited by 10 · see citing works
Krogh, T. J., Franke, A., Møller‐Jensen, J., & Kaleta, C. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.02002 · Cited by 5 · see citing works
Anvar, S. Y., Tucker, A., Vinciotti, V., Venema, A., van Ommen, G. B., van der Maarel, S. M., Raz, V., & Hoen, P. A. C. ‘. (2011) · PLoS Computational Biology · doi:10.1371/annotation/fc0b4192-6427-4fb3-b347-c66651adf855 · Cited by 5 · see citing works
Correcting positional correlations in Affymetrix® Genome Chips
Homouz, D., Chen, G., & Kudlicki, A. S. (2015) · Scientific Reports · doi:10.1038/srep09078 · Cited by 4 · see citing works
Quality assessment for short oligonucleotide microarray dataPreprint
Brettschneider, J. A., Collin, F., Bolstad, B. M., & Speed, T. P. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0710.0178 · Cited by 3 · see citing works
Customizing microarrays for neuroscience drug discovery
Girgenti, M. J., & Newton, S. S. (2007) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2.8.1139
Modelling genetic and genomic interactions underlying gene expression and complex traits
Langley, S. R. (2012) · Spiral (Imperial College London) · doi:10.25560/10925
Reconstructing Signaling Pathways from High Throughput DataPreprint
Zhu, D. (2006)
Bayesian Hierarchical Model for Large-Scale Covariance Matrix EstimationPreprint
Zhua, D. (2006)
Chromosomal Spatial Correlation of Gene Expression in Plasmodium falciparum
Christian, J. B., Shaw, C. A., Noyola-Martinez, J., Gustin, M. C., Scott, D. W., & Guerra, R. (2007) · Springer eBooks · doi:10.1007/978-0-387-34569-7_11
- Authors
- Kluger, Y., Yu, H., Qian, J., & Gerstein, M.
- Affiliations
- Yale University (US)
- Johns Hopkins University (US)
- Corresponding author
- Gerstein, Mark
- Venue
- BMC Genomics
- Published
- 2003-12-10
- Volume
- 4
- Issue
- 1
- Pages
- 49-49
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC317287
- OpenAlex
- W2119504746
- Cited by
- 36
- References
- 19
- Citations by year
- 2022: 1 · 2020: 1 · 2017: 1 · 2016: 1 · 2015: 1 · 2012: 2
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Genomics (publishedVersion)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- 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 Chromatin Dynamics
- Keywords
- gene co-expression, spatial artifacts, gene clustering, yeast, Caenorhabditis elegans, Drosophila melanogaster, gene expression profiling, periodicity
- MeSH terms
- Animals, Chromosome Mapping, Drosophila melanogaster, Escherichia coli, Saccharomyces cerevisiae, Oligonucleotide Probes, Artifacts, Caenorhabditis elegans, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Gene Order
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