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Use of within-array replicate spots for assessing differential expression in microarray experiments
Abstract
MOTIVATION: Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. The usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This results in the loss of valuable information about genewise variability. RESULTS: A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. AVAILABILITY: The methodology is implemented in the limma software package for R, available from the CRAN repository http://www.r-project.org
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Cites
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
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Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
Self-citationlimma: Linear Models for Microarray Data
Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796
Self-citationProgrammed cell senescence during mammalian embryonic development
Muñoz-Espín, D., Cañamero, M., Maraver, A., Gómez-López, G., Contreras, J., Murillo-Cuesta, S., Rodríguez-Baeza, A., Varela-Nieto, I., Ruberte, J., Collado, M., et al. (2013) · Cell · doi:10.1016/j.cell.2013.10.019 · Cited by 1,428
Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), Savas, P., Virassamy, B., Ye, C., Salim, A., Mintoff, C. P., Caramia, F., Salgado, R., Byrne, D. J., Teo, Z. L., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0078-7 · Cited by 992
Nagahara, A. H., Merrill, D. A., Coppola, G., Tsukada, S., Schroeder, B. E., Shaked, G. M., Wang, L., Blesch, A., Kim, A., Conner, J. M., et al. (2009) · Nature Medicine · doi:10.1038/nm.1912 · Cited by 1,034
INTERFEROME v2.0: an updated database of annotated interferon-regulated genes
Rusinova, I., Forster, S., Yu, S., Kannan, A., Masse, M., Cumming, H., Chapman, R., & Hertzog, P. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1215 · Cited by 878
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Ritchie, M. E., Silver, J., Oshlack, A., Holmes, M., Diyagama, D., Holloway, A., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm412 · Cited by 907
Self-citationControl of mammary stem cell function by steroid hormone signalling
Asselin-Labat, M.-L., Vaillant, F., Sheridan, J. M., Pal, B., Wu, D., Simpson, E. R., Yasuda, H., Smyth, G. K., Martin, T. J., Lindeman, G. J., et al. (2010) · Nature · doi:10.1038/nature09027 · Cited by 693
Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease
Gjoneska, E., Pfenning, A. R., Mathys, H., Quon, G., Kundaje, A., Tsai, L.-H., & Kellis, M. (2015) · Nature · doi:10.1038/nature14252 · Cited by 576
ROAST: rotation gene set tests for complex microarray experiments
Wu, D., Lim, E., Vaillant, F., Asselin-Labat, M.-L., Visvader, J. E., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq401 · Cited by 560
Self-citationWeydt, P., Pineda, V. V., Torrence, A. E., Libby, R. T., Satterfield, T. F., Lazarowski, E. R., Gilbert, M. L., Morton, G. J., Bammler, T. K., Strand, A. D., et al. (2006) · Cell Metabolism · doi:10.1016/j.cmet.2006.10.004 · Cited by 562
Bain, C. C., Hawley, C. A., Garner, H., Scott, C. L., Schridde, A., Steers, N. J., Mack, M., Joshi, A., Guilliams, M., Mowat, A. M. I., et al. (2016) · Nature Communications · doi:10.1038/ncomms11852 · Cited by 527
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515
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Pandolfini, L., Barbieri, I., Bannister, A. J., Hendrick, A., Andrews, B., Webster, N., Murat, P., Mach, P., Brandi, R., Robson, S. C., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.03.040 · Cited by 491
Lim, E., Wu, D., Pal, B., Bouras, T., Asselin-Labat, M.-L., Vaillant, F., Yagita, H., Lindeman, G. J., Smyth, G. K., & Visvader, J. E. (2010) · Breast Cancer Research · doi:10.1186/bcr2560 · Cited by 440
Self-citationBatch effects and the effective design of single-cell gene expression studies
Tung, P.-Y., Blischak, J. D., Hsiao, C. J., Knowles, D. A., Burnett, J. E., Pritchard, J. K., & Gilad, Y. (2017) · Scientific Reports · doi:10.1038/srep39921 · Cited by 356
A Pericellular Collagenase Directs the 3-Dimensional Development of White Adipose Tissue
Chun, T.-H., Hotary, K. B., Sabeh, F., Saltiel, A. R., Allen, E. D., & Weiss, S. J. (2006) · Cell · doi:10.1016/j.cell.2006.02.050 · Cited by 378
Guijarro-Muñoz, I., Compte, M., Álvarez-Cienfuegos, A., Álvarez-Vallina, L., & Sanz, L. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.521161 · Cited by 342
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Li, S., Overman, J. J., Katsman, D., Kozlov, S. V., Donnelly, C. J., Twiss, J. L., Giger, R. J., Coppola, G., Geschwind, D. H., & Carmichael, S. T. (2010) · Nature Neuroscience · doi:10.1038/nn.2674 · Cited by 329
Dream: powerful differential expression analysis for repeated measures designs
Hoffman, G. E., & Roussos, P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa687 · Cited by 319
Franjic, D., Skarica, M., Ma, S., Arellano, J. I., Tebbenkamp, A. T. N., Choi, J., Xu, C., Li, Q., Morozov, Y. M., Andrijevic, D., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.10.036 · Cited by 308
Veloso, A., Kirkconnell, K. S., Magnuson, B., Biewen, B., Paulsen, M. T., Wilson, T. E., & Ljungman, M. (2014) · Genome Research · doi:10.1101/gr.171405.113 · Cited by 281
Foxp2 regulates gene networks implicated in neurite outgrowth in the developing brain
Vernes, S. C., Oliver, P. L., Spiteri, E., Lockstone, H. E., Puliyadi, R., Taylor, J. M., Ho, J., Mombereau, C., Brewer, A., Lowy, E., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002145 · Cited by 276
Kemmeren, P., Sameith, K., van de Pasch, L. A. L., Benschop, J. J., Lenstra, T. L., Margaritis, T., O’Duibhir, E., Apweiler, E., van Wageningen, S., Ko, C. W., et al. (2014) · Cell · doi:10.1016/j.cell.2014.02.054 · Cited by 277
Hacquard, S., Kracher, B., Hiruma, K., Münch, P. C., Garrido-Oter, R., Thon, M. R., Weimann, A., Damm, U., Dallery, J.-F., Hainaut, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11362 · Cited by 278
Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data
Lun, A. T. L., & Marioni, J. C. (2017) · Biostatistics · doi:10.1093/biostatistics/kxw055 · Cited by 159
HOXA5 Counteracts Stem Cell Traits by Inhibiting Wnt Signaling in Colorectal Cancer
Ordóñez-Morán, P., Dafflon, C., Imajo, M., Nishida, E., & Huelsken, J. (2015) · Cancer Cell · doi:10.1016/j.ccell.2015.11.001 · Cited by 209
Edwards, D. K., Jasny, E., Yoon, H., Horscroft, N., Schanen, B., Geter, T., Fotin-Mleczek, M., Petsch, B., & Wittman, V. (2017) · Journal of Translational Medicine · doi:10.1186/s12967-016-1111-6 · Cited by 186
Hongisto, V., Jernström, S., Fey, V., Mpindi, J.-P., Kleivi Sahlberg, K., Kallioniemi, O., & Perälä, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077232 · Cited by 181
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Qin, Y., Garrison, B. S., Ma, W., Wang, R., Jiang, A., Li, J., Mistry, M., Bronson, R. T., Santoro, D., Franco, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.027 · Cited by 180
Bär, C., de Jesus, B. B., Serrano, R., Tejera, A., Ayuso, E., Jimenez, V., Formentini, I., Bobadilla, M., Mizrahi, J., de Martino, A., et al. (2014) · Nature Communications · doi:10.1038/ncomms6863 · Cited by 162
Haas, J. T., Vonghia, L., Mogilenko, D. A., Verrijken, A., Molendi-Coste, O., Fleury, S., Deprince, A., Nikitin, A., Woitrain, E., Ducrocq-Geoffroy, L., et al. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0076-1 · Cited by 155
Moon, J. S., Goeminne, L. J. E., Kim, J. T., Tian, J. W., Kim, S., Nga, H. T., Kang, S. G., Kang, B. E., Byun, J., Lee, Y., et al. (2020) · Aging Cell · doi:10.1111/acel.13195 · Cited by 141
Functional Overlap and Regulatory Links Shape Genetic Interactions between Signaling Pathways
van Wageningen, S., Kemmeren, P., Lijnzaad, P., Margaritis, T., Benschop, J. J., de Castro, I. J., van Leenen, D., Groot Koerkamp, M. J. A., Ko, C. W., Miles, A. J., et al. (2010) · Cell · doi:10.1016/j.cell.2010.11.021 · Cited by 133
Disturbed glucose and pyruvate metabolism in glaucoma with neuroprotection by pyruvate or rapamycin
Harder, J. M., Guymer, C., Wood, J. P. M., Daskalaki, E., Chidlow, G., Zhang, C., Balasubramanian, R., Cardozo, B. H., Foxworth, N. E., Deering, K. E., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2014213117 · Cited by 127
“Data were analyzed in R using limma (linear models for microarray data, designed for microarray analysis but suitable for other data types) (51 – 53).”
Matsoso, L. G., Kana, B. D., Crellin, P. K., Lea-Smith, D. J., Pelosi, A., Powell, D., Dawes, S. S., Rubin, H., Coppel, R. L., & Mizrahi, V. (2005) · Journal of Bacteriology · doi:10.1128/jb.187.18.6300-6308.2005 · Cited by 134
Wilson, S., Qi, J., & Filipp, F. V. (2016) · Scientific Reports · doi:10.1038/srep32611 · Cited by 126
Xia, D., Lianoglou, S., Sandmann, T., Calvert, M., Suh, J. H., Thomsen, E., Dugas, J., Pizzo, M. E., DeVos, S. L., Earr, T. K., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00547-7 · Cited by 124
A robust neuromuscular system protects rat and human skeletal muscle from sarcopenia
Pannérec, A., Springer, M., Migliavacca, E., Ireland, A., Piasecki, M., Karaz, S., Jacot, G., Métairon, S., Danenberg, E., Raymond, F., et al. (2016) · Aging · doi:10.18632/aging.100926 · Cited by 110
Compensatory Evolution of Gene Regulation in Response to Stress by Escherichia coli Lacking RpoS
Stoebel, D. M., Hokamp, K., Last, M. S., & Dorman, C. J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000671 · Cited by 99
Vasanthakumar, A., Davis, J. W., Idler, K., Waring, J. F., Asque, E., Riley-Gillis, B., Grosskurth, S., Srivastava, G., Kim, S., Nho, K., et al. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00864-y · Cited by 98
Plasma proteomics identifies a ‘chemokine storm’ in idiopathic multicentric Castleman disease
Pierson, S. K., Stonestrom, A. J., Shilling, D., Ruth, J., Nabel, C. S., Singh, A., Ren, Y., Stone, K., Li, H., van Rhee, F., et al. (2018) · American Journal of Hematology · doi:10.1002/ajh.25123 · Cited by 90
Wang, Y., Zhao, Z.-J., Kang, X.-R., Bian, T., Shen, Z.-M., Jiang, Y., Sun, B., Hu, H.-B., & Chen, Y.-S. (2020) · Aging · doi:10.18632/aging.104095 · Cited by 85
Determining the ERK-regulated phosphoproteome driving KRAS-mutant cancer
Klomp, J. E., Diehl, J. N., Klomp, J. A., Edwards, A. C., Yang, R., Morales, A. J., Taylor, K. E., Drizyte-Miller, K., Bryant, K. L., Schaefer, A., et al. (2024) · Science · doi:10.1126/science.adk0850 · Cited by 83
Cheng, K. C., Beaulieu, J., Iquira, E., Belzile, F. J., Fortin, M. G., & Strömvik, M. V. (2008) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf073505i · Cited by 76
Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences
Zhang, B., Pirmoradian, M., Zubarev, R., & Käll, L. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o117.067728 · Cited by 68
Transcriptomic profiles of aging in purified human immune cells
Reynolds, L. M., Ding, J., Taylor, J. R., Lohman, K., Soranzo, N., de la Fuente, A., Liu, T. F., Johnson, C., Barr, R. G., Register, T. C., et al. (2015) · BMC Genomics · doi:10.1186/s12864-015-1522-4 · Cited by 62
Anterior-posterior differences in HoxD chromatin topology in limb development
Williamson, I., Eskeland, R., Lettice, L. A., Hill, A. E., Boyle, S., Grimes, G. R., Hill, R. E., & Bickmore, W. A. (2012) · Development · doi:10.1242/dev.081174 · Cited by 62
Walker, J. M., Kazempour Dehkordi, S., Fracassi, A., Vanschoiack, A., Pavenko, A., Taglialatela, G., Woltjer, R., Richardson, T. E., Zare, H., & Orr, M. E. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01324-9 · Cited by 59
Stojcheva, N., Schechtmann, G., Sass, S., Roth, P., Florea, A.-M., Stefanski, A., Stühler, K., Wolter, M., Müller, N. S., Theis, F. J., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.7346 · Cited by 50
Matrigel Basement Membrane Matrix influences expression of microRNAs in cancer cell lines
Price, K. J., Tsykin, A., Giles, K. M., Sladic, R. T., Epis, M. R., Ganss, R., Goodall, G. J., & Leedman, P. J. (2012) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2012.09.059 · Cited by 49
An Antifungal Benzimidazole Derivative Inhibits Ergosterol Biosynthesis and Reveals Novel Sterols
Keller, P., Müller, C., Engelhardt, I., Hiller, E., Lemuth, K., Eickhoff, H., Wiesmüller, K.-H., Burger-Kentischer, A., Bracher, F., & Rupp, S. (2015) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00640-15 · Cited by 50
Chondrocytes Play a Major Role in the Stimulation of Bone Growth by Thyroid Hormone
Desjardin, C., Charles, C., Benoist-Lasselin, C., Riviere, J., Gilles, M., Chassande, O., Morgenthaler, C., Laloé, D., Lecardonnel, J., Flamant, F., et al. (2014) · Endocrinology · doi:10.1210/en.2014-1109 · Cited by 35
He, B. Z., Zhou, X., & O’Shea, E. K. (2017) · eLife · doi:10.7554/elife.25157 · Cited by 27
Torsvik, A., Brattbakk, H.-R., Trentani, A., Holdhus, R., Stansberg, C., Bartz-Johannessen, C. A., Hughes, T., Steen, N. E., Melle, I., Djurovic, S., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02442-1 · Cited by 21
Czepukojc, B., Abuhaliema, A., Barghash, A., Tierling, S., Naß, N., Simon, Y., Körbel, C., Cadenas, C., van Hul, N., Sachinidis, A., et al. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00179 · Cited by 20
McHugo, G. P., Ward, J. A., Browne, J. A., O’Gorman, G. M., Meade, K. G., Hill, E. W., Hall, T. J., & MacHugh, D. E. (2025) · doi:10.1101/2025.01.31.635858 · Cited by 4
Maione, A. S., Iengo, L., Sala, L., Massaiu, I., Chiesa, M., Lippi, M., Ghilardi, S., Florindi, C., Lodola, F., Zaza, A., et al. (2024) · Cell Death Discovery · doi:10.1038/s41420-024-02232-8 · Cited by 4
Metadata
- Authors
- Smyth, G. K., Michaud, J., & Scott, H. S.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-01-18
- Volume
- 21
- Issue
- 9
- Pages
- 2067-2075
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2142415307
- Cited by
- 1,405
- References
- 2
- FWCI
- 19.13
- Type
- journal article
- Language
- en
- Open access
- closed
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