Positional artifacts in microarrays: experimental verification and construction of COP, an automated detection tool
Open accessAbstract
Microarray technology is currently one of the most widely-used technologies in biology. Many studies focus on inferring the function of an unknown gene from its co-expressed genes. Here, we are able to show that there are two types of positional artifacts in microarray data introducing spurious correlations between genes. First, we find that genes that are close on the microarray chips tend to have higher correlations between their expression profiles. We call this the 'chip artifact'. Our calculations suggest that the carry-over during the printing process is one of the major sources of this type of artifact, which is later confirmed by our experiments. Based on our experiments, the measured intensity of a microarray spot contains 0.1% (for fully-hybridized spots) to 93% (for un-hybridized ones) of noise resulting from this artifact. Secondly, we, for the first time, show that genes that are close on the microtiter plates in microarray experiments also tend to have higher correlations. We call this the 'plate artifact'. Both types of artifacts exist with different severity in all cDNA microarray experiments that we analyzed. Therefore, we develop an automated web tool-COP (COrrelations by Positional artifacts) to detect these artifacts in microarray experiments. COP has been integrated with the microarray data normalization tool, ExpressYourself, which is available at http://bioinfo.mbb.yale.edu/ExpressYourself/. Together, the two can eliminate most of the common noises in microarray data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Positional effects due to technical variability manifest as distinct spatial patterns across rows, columns and edges in different plates.
“In contrast, positional effects due to technical variability manifest as distinct spatial patterns across the rows, columns and edges in different plates, a common challenge in multi-well-based assays30–33.”
From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [31]
In Results and Discussion, “Artifact related to positions on microtiter plates” (page unavailable), the paper reports artificially higher correlations among nearby plate positions, linked to cross-contamination, and notes an effect along the Y-direction. It does not describe distinct patterns across rows, columns, and edges of different plates. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPositional 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 [31]
The Results and Discussion sections on plate artifacts and their consequences report positional artifacts in every microarray experiment examined, including effects associated with locations on microtiter plates. The paper does not establish that these effects persist across multi-well assays generally, or specifically document edge effects and gradient artifacts. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome Biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 13,133 · see citing works
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
Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray
Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995) · Science · doi:10.1126/science.270.5235.467 · Cited by 9,609 · 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
Transcriptional Regulatory Networks in <i>Saccharomyces cerevisiae</i>
Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) · Science · doi:10.1126/science.1075090 · Cited by 2,956 · 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
Gautier, J. (1993) · Trends in Biochemical Sciences · doi:10.1016/0968-0004(93)90025-i · Cited by 2,173 · 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
Microarray data normalization and transformation
Quackenbush, J. (2002) · Nature Genetics · doi:10.1038/ng1032 · Cited by 1,975 · see citing works
Normalization of cDNA microarray data
Smyth, G. K., & Speed, T. (2003) · Methods · doi:10.1016/s1046-2023(03)00155-5 · Cited by 1,871 · see citing works
Shalon, D., Smith, S. J., & Brown, P. O. (1996) · Genome Research · doi:10.1101/gr.6.7.639 · Cited by 1,122 · see citing works
Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF
Iyer, V. R., Horak, C. E., Scafe, C., Botstein, D., Snyder, M. P., & Brown, P. O. (2001) · Nature · doi:10.1038/35054095 · Cited by 1,084 · see citing works
Self-citationCohen, 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
A Role for SIR-2.1 Regulation of ER Stress Response Genes in Determining C. elegans Life Span
Viswanathan, M., Kim, S. K., Berdichevsky, A., & Guarente, L. (2005) · Developmental Cell · doi:10.1016/j.devcel.2005.09.017 · Cited by 409 · 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
Molecular biology of the cell (3rd edn)
Grimstone, A. V. (1995) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(00)89011-8 · Cited by 344 · see citing works
Dudley, A. M., Aach, J., Steffen, M. A., & Church, G. M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.112683499 · Cited by 248 · see citing works
Transcriptional Regulatory Networks in Saccharomyces cerevisiae
Kraal, L. (2009) · Utrecht University Repository (Utrecht University) · Cited by 209 · see citing works
Whole-genome expression analysis: challenges beyond clustering
Altman, R. B., & Raychaudhuri, S. (2001) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(00)00212-8 · Cited by 158 · see citing works
Dunn, B. L., Levine, R. P., & Sherlock, G. J. (2005) · BMC Genomics · doi:10.1186/1471-2164-6-53 · Cited by 135 · 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-citationA Gene Expression Signature of Genetic Instability in Colon Cancer
Giacomini, C. P., Leung, S. Y., Chen, X., Yuen, S. T., Kim, Y. H., Bair, E., & Pollack, J. R. (2005) · Cancer Research · doi:10.1158/0008-5472.can-04-4163 · Cited by 74 · see citing works
Spurious spatial periodicity of co-expression in microarray data due to printing design
Balázsi, G. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg485 · Cited by 43 · see citing works
ExpressYourself: a modular platform for processing and visualizing microarray data
Luscombe, N. M. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg628 · Cited by 40 · see citing works
Relationship between gene co-expression and probe localization on microarray slides
Kluger, Y., Yu, H., Qian, J., & Gerstein, M. (2003) · BMC Genomics · doi:10.1186/1471-2164-4-49 · Cited by 36 · see citing works
Self-citationIdentification 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
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
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 .”
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
- Authors
- Yu, H., Nguyen, K., Royce, T., Qian, J., Nelson, K., Snyder, M., & Gerstein, M.
- Affiliations
- Yale University (US)
- Corresponding author
- Gerstein, M.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-12-14
- Volume
- 35
- Issue
- 2
- Pages
- e8-e8
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC1802630
- OpenAlex
- W2146506607
- Cited by
- 13
- References
- 31
- Citations by year
- 2023: 1 · 2022: 2 · 2017: 1 · 2016: 2 · 2015: 1 · 2013: 1 · 2012: 2
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- 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, co-expression analysis, cDNA microarray, COP, data normalization
- MeSH terms
- Animals, Humans, Software, Artifacts, Internet, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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
