Optimizing the noise versus bias trade-off for Illumina Whole Genome Expression Beadchips
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Five strategies for pre-processing intensities from Illumina expression BeadChips are assessed from the point of view of precision and bias. The strategies include a popular variance stabilizing transformation and model-based background corrections that either use or ignore the control probes. Four calibration data sets are used to evaluate precision, bias and false discovery rate (FDR). The original algorithms are shown to have operating characteristics that are not easily comparable. Some tend to minimize noise while others minimize bias. Each original algorithm is shown to have an innate intensity offset, by which unlogged intensities are bounded away from zero, and the size of this offset determines its position on the noise-bias spectrum. By adding extra offsets, a continuum of related algorithms with different noise-bias trade-offs is generated, allowing direct comparison of the performance of the strategies on equivalent terms. Adding a positive offset is shown to decrease the FDR of each original algorithm. The potential of each strategy to generate an algorithm with an optimal noise-bias trade-off is explored by finding the offset that minimizes its FDR. The use of control probes as part of the background correction and normalization strategy is shown to achieve the lowest FDR for a given bias.
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Offsets in a range of moderate values have been shown to achieve an effective compromise between noise and bias.
“Offsets in a range of moderate values have been shown to achieve an effective compromise between noise and bias (24).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [24]
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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 · see citing works
Self-citationExploration, normalization, and summaries of high density oligonucleotide array probe level data
Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603 · see citing works
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,185 · see citing works
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066 · see citing works
limma: 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 · see citing works
Self-citationSummaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,906 · see citing works
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Gentleman, R. C., Carey, V. J., Huber, W. K., Irizarry, R. A., & Dudoit, S. (2005) · Statistics in the health sciences · doi:10.1007/0-387-29362-0 · Cited by 3,470 · see citing works
Algorithms for Minimization Without Derivatives
G., D., & Brent, R. P. (1974) · Mathematics of Computation · doi:10.2307/2005713 · Cited by 2,914 · see citing works
MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126 · see citing works
lumi : a pipeline for processing Illumina microarray
Du, P., Kibbe, W. A., & Lin, S. M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn224 · Cited by 2,028 · see citing works
A Model-Based Background Adjustment for Oligonucleotide Expression Arrays
Wu, Z. J., Irizarry, R. A., Gentleman, R. C., Martínez-Murillo, F., & Spencer, F. A. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000683 · Cited by 1,717 · see citing works
kConFab, Lim, E., Vaillant, F., Wu, D., Forrest, N. C., Pal, B., Hart, A. H., Asselin-Labat, M.-L., Gyorki, D. E., Ward, T., et al. (2009) · Nature Medicine · doi:10.1038/nm.2000 · Cited by 1,492 · cited 3× · see citing works
“The idea of offsetting intensity data has been discussed previously as an approximate variance-stabilizing strategy in the context of two-color microarray data (6,17), and has also been used for Illumina data (9,10,13,16).”
BackgroundMethodSelf-citationAlgorithms for minimization without derivatives
Anderson, D. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100629 · Cited by 1,215 · see citing works
A comparison of background correction methods for two-colour microarrays
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 · see citing works
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 · see citing works
Self-citationBrent, R. P. (1975) · Mathematics of Computation · doi:10.1090/s0025-5718-1975-0371062-9 · Cited by 608 · see citing works
Model-based variance-stabilizing transformation for Illumina microarray data
Lin, S. M., Du, P., Huber, W. K., & Kibbe, W. A. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkm1075 · Cited by 515 · see citing works
beadarray: R classes and methods for Illumina bead-based data
Dunning, M., Smith, M. L., Ritchie, M. E., & Tavaré, S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm311 · Cited by 462 · see citing works
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 · see citing works
Self-citationEstimating the Proportion of True Null Hypotheses, with application to DNA Microarray Data
Langaas, M., Lindqvist, B. H., & Ferkingstad, E. (2005) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2005.00515.x · Cited by 316 · see citing works
Filtering for increased power for microarray data analysis
Hackstadt, A. J., & Hess, A. M. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-11 · Cited by 291 · see citing works
Barnes, M. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki890 · Cited by 253 · see citing works
Silver, J. D., Ritchie, M. E., & Smyth, G. K. (2008) · Biostatistics · doi:10.1093/biostatistics/kxn042 · Cited by 208 · see citing works
Self-citationApproximate variance-stabilizing transformations for gene-expression microarray data
Rocke, D. M., & Durbin, B. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg107 · Cited by 160 · see citing works
Statistical issues in the analysis of Illumina data
Dunning, M., Barbosa‐Morais, N. L., Lynch, A. G., Tavaré, S., & Ritchie, M. E. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-85 · Cited by 104 · see citing works
Statistical methods of background correction for Illumina BeadArray data
Xie, Y., Wang, X., & Story, M. D. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp040 · Cited by 83 · see citing works
Ding, L.-H., Xie, Y., Park, S., Xiao, G., & Story, M. D. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn234 · Cited by 65 · see citing works
Consolidated strategy for the analysis of microarray spike-in data
McCall, M. N., & Irizarry, R. A. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn430 · Cited by 34 · see citing works
Detection call algorithms for high-throughput gene expression microarray data
Archer, K. J., & Reese, S. E. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp055 · Cited by 32 · see citing works
Illumina WG-6 BeadChip strips should be normalized separately
Shi, W., Banerjee, A., Ritchie, M. E., Gerondakis, S., & Smyth, G. K. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-372 · Cited by 29 · see citing works
Self-citationEstimating the proportion of microarray probes expressed in an RNA sample
Shi, W., de Graaf, C. A., Kinkel, S., Achtman, A. H., Baldwin, T. M., Schofield, L., Scott, H. S., Hilton, D. J., & Smyth, G. K. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp1204 · Cited by 23 · see citing works
Self-citationSpike-in validation of an Illumina-specific variance-stabilizing transformation
Dunning, M., Ritchie, M. E., Barbosa‐Morais, N. L., Tavaré, S., & Lynch, A. G. (2008) · BMC Research Notes · doi:10.1186/1756-0500-1-18 · Cited by 14 · see citing works
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limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
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Liao, Y., Smyth, G. K., & Shi, W. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt214 · Cited by 3,064 · see citing works
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Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479 · see citing works
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Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S., & Smyth, G. K. (2016) · The Annals of Applied Statistics · doi:10.1214/16-aoas920 · Cited by 966 · see citing works
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White, M. J., McArthur, K., Metcalf, D., Lane, R. M., Cambier, J. C., Herold, M. J., van Delft, M. F., Bedoui, S., Lessene, G., Ritchie, M. E., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.036 · Cited by 983 · see citing works
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Triche, T. J., Weisenberger, D. J., Berg, D. V. D., Laird, P. W., & Siegmund, K. D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt090 · Cited by 885 · see citing works
Camera: a competitive gene set test accounting for inter-gene correlation
Wu, D., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks461 · Cited by 880 · see citing works
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Schuijt, T. J., Lankelma, J. M., Scicluna, B. P., de Sousa e Melo, F., Roelofs, J. J. T. H., de Boer, J. D., Hoogendijk, A. J., de Beer, R., de Vos, A., Belzer, C., et al. (2016) · Gut · doi:10.1136/gutjnl-2015-309728 · Cited by 832 · see citing works
Cretney, E., Xin, A., Shi, W., Minnich, M., Masson, F., Miasari, M., Belz, G. T., Smyth, G. K., Busslinger, M., Nutt, S. L., et al. (2011) · Nature Immunology · doi:10.1038/ni.2006 · Cited by 605 · see citing works
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Goujon, C., Moncorgé, O., Bauby, H., Doyle, T., Ward, C. C., Schaller, T., Hué, S., Barclay, W., Schulz, R., & Malim, M. H. (2013) · Nature · doi:10.1038/nature12542 · Cited by 587 · see citing works
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Bunse, L., Pusch, S., Bunse, T., Sahm, F., Sanghvi, K., Friedrich, M., Alansary, D., Sonner, J. K., Green, E., Deumelandt, K., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0095-6 · Cited by 544 · see citing works
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Hombrink, P., Helbig, C., Backer, R. A., Piet, B., Oja, A. E., Stark, R., Brasser, G., Jongejan, A., Jonkers, R. E., Nota, B., et al. (2016) · Nature Immunology · doi:10.1038/ni.3589 · Cited by 455 · see citing works
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Bai, H., Harmancı, A. S., Erson-Omay, Z., Li, J., Coşkun, S., Simon, M., Krischek, B., Özduman, K., Omay, S. B., Sorensen, E. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3457 · Cited by 324 · see citing works
Stančáková, A., Civelek, M., Saleem, N. K., Soininen, P., Kangas, A. J., Cederberg, H., Paananen, J., Pihlajamäki, J., Bonnycastle, L. L., Morken, M. A., et al. (2012) · Diabetes · doi:10.2337/db11-1378 · Cited by 279 · see citing works
Dittrich, M. T., Mueller, H. M., Bauer, H., Peirats‐Llobet, M., Rodrı́guez, P. L., Geilfus, C., Carpentier, S., Rasheid, K. A. S. A., Kollist, H., Merilo, E., et al. (2019) · Nature Plants · doi:10.1038/s41477-019-0490-0 · Cited by 213 · see citing works
Histone variant H2A.J accumulates in senescent cells and promotes inflammatory gene expression
Contrepois, K., Coudereau, C., Benayoun, B. A., Schuler, N., Roux, P.-F., Bischof, O., Courbeyrette, R., Carvalho, C., Thuret, J.-Y., Ma, Z., et al. (2017) · Nature Communications · doi:10.1038/ncomms14995 · Cited by 191 · see citing works
Xin, A., Masson, F., Liao, Y., Preston, S., Guan, T., Gloury, R., Olshansky, M., Lin, J., Li, P., Speed, T. P., et al. (2016) · Nature Immunology · doi:10.1038/ni.3410 · Cited by 185 · see citing works
Self-citationBilberries reduce low‐grade inflammation in individuals with features of metabolic syndrome
Kolehmainen, M., Mykkänen, O. T., Kirjavainen, P. V., Leppänen, T., Moilanen, E., Adriaens, M. E., Laaksonen, D. E., Hallikainen, M., Puupponen‐Pimiä, R., Pulkkinen, L., et al. (2012) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201200195 · Cited by 184 · see citing works
Cordell, H. J., Bentham, J., Töpf, A., Zélénika, D., Heath, S. C., Mamasoula, C., Cosgrove, C., Blue, G. M., Granados-Riveron, J. T., Setchfield, K., et al. (2013) · Nature Genetics · doi:10.1038/ng.2637 · Cited by 163 · see citing works
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Oja, A. E., Piet, B., Helbig, C., Stark, R., van der Zwan, D., Blaauwgeers, H., Remmerswaal, E. B. M., Amsen, D., Jonkers, R. E., Moerland, P. D., et al. (2018) · Mucosal Immunology · doi:10.1038/mi.2017.94 · Cited by 160 · see citing works
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Egea, O. I., Gálvez‐Montón, C., Roura, S., Perea‐Gil, I., Prat‐Vidal, C., Soler‐Botija, C., & Bayés‐Genís, A. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0013-4 · Cited by 137 · see citing works
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Fu, N. Y., Rios, A. C., Pal, B., Law, C. W., Jamieson, P. R., Liu, R., Vaillant, F., Jackling, F. C., Liu, K. H., Smyth, G. K., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3471 · Cited by 134 · see citing works
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Pal, B., Bouras, T., Shi, W., Vaillant, F., Sheridan, J. M., Fu, N. Y., Breslin, K. A., Jiang, K., Ritchie, M. E., Young, M. D., et al. (2013) · Cell Reports · doi:10.1016/j.celrep.2012.12.020 · Cited by 132 · see citing works
Self-citationCabrera, C., Manson, J., Shepherd, J. M., Torrance, H. D. T., Watson, D., Longhi, M. P., Hoti, M., Patel, M. B., O’Dwyer, M. J., Nourshargh, S., et al. (2017) · PLoS Medicine · doi:10.1371/journal.pmed.1002352 · Cited by 123 · see citing works
Association of Ketone Body Levels With Hyperglycemia and Type 2 Diabetes in 9,398 Finnish Men
Mahendran, Y., Vangipurapu, J., Cederberg, H., Stančáková, A., Pihlajamäki, J., Soininen, P., Kangas, A. J., Paananen, J., Civelek, M., Saleem, N. K., et al. (2013) · Diabetes · doi:10.2337/db12-1363 · Cited by 122 · see citing works
Chevrier, S., Emslie, D., Shi, W., Kratina, T., Wellard, C. J., Karnowski, A., Erikçi, E., Smyth, G. K., Chowdhury, K., Tarlinton, D. M., et al. (2014) · The Journal of Experimental Medicine · doi:10.1084/jem.20131831 · Cited by 118 · see citing works
Self-citationCroker, B. A., O’Donnell, J. A., Nowell, C. J., Metcalf, D., Dewson, G., Campbell, K. J., Rogers, K. L., Hu, Y., Smyth, G. K., Zhang, J., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110358108 · Cited by 116 · see citing works
Self-citationAsselin-Labat, M.-L., Sutherland, K. D., Vaillant, F., Gyorki, D. E., Wu, D., Holroyd, S. L., Breslin, K. A., Ward, T. A., Shi, W., Bath, M. L., et al. (2011) · Molecular and Cellular Biology · doi:10.1128/mcb.05766-11 · Cited by 114 · see citing works
Self-citationIanculescu, I., Wu, D., Siegmund, K. D., & Stallcup, M. R. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.300194 · Cited by 106 · see citing works
Lacruz, R. S., Smith, C. E., Bringas, P., Chen, Y., Smith, S. M., Snead, M. L., Kurtz, I., Hacia, J. G., Hubbard, M. J., & Paine, M. L. (2011) · Journal of Cellular Physiology · doi:10.1002/jcp.22965 · Cited by 103 · see citing works
Achtman, A. H., Pilat, S., Law, C. W., Lynn, D. J., Janot, L., Mayer, M. L., Ma, S., Kindrachuk, J., Finlay, B. B., Brinkman, F. S. L., et al. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003515 · Cited by 94 · see citing works
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Tiffen, J. C., Gunatilake, D., Gallagher, S. J., Gowrishankar, K., Heinemann, A., Cullinane, C. M., Dutton‐Regester, K., Pupo, G. M., Strbenac, D., Yang, J. Y. H., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.4809 · Cited by 81 · see citing works
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Stanifer, M. L., Mukenhirn, M., Muenchau, S., Pervolaraki, K., Kanaya, T., Albrecht, D., Odendall, C., Hielscher, T., Haucke, V., Kagan, J. C., et al. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0594-3 · Cited by 65 · see citing works
Erg is required for self-renewal of hematopoietic stem cells during stress hematopoiesis in mice
Ng, A. P., Loughran, S. J., Metcalf, D., Hyland, C. D., de Graaf, C. A., Hu, Y., Smyth, G. K., Hilton, D. J., Kile, B. T., & Alexander, W. S. (2011) · Blood · doi:10.1182/blood-2011-03-344739 · Cited by 65 · see citing works
Self-citationSahu, A., Köse, K., Kraehenbuehl, L., Byers, C., Holland, A., Tembo, T., Santella, A., Alfonso, A., Li, M., Cordova, M. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32738-7 · Cited by 62 · see citing works
Pang, S. H. M., de Graaf, C. A., Hilton, D. J., Huntington, N. D., Carotta, S., Wu, L., & Nutt, S. L. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01264 · Cited by 60 · see citing works
“Raw intensities were normalized by using the neqc function, which performs background and quantile normalization using control probes (40).”
MethodMorales, A. J., Carrero, J. A., Hung, P. J., Tubbs, A., Andrews, J. M., Edelson, B. T., Calderón, B., Innes, C. L., Paules, R. S., Payton, J. E., et al. (2017) · eLife · doi:10.7554/elife.24655 · Cited by 60 · see citing works
In vitro three-dimensional modeling of fallopian tube secretory epithelial cells
Lawrenson, K., Notaridou, M., Lee, N., Benjamin, E. O., Jacobs, I. J., Jones, C., & Gayther, S. A. (2013) · BMC Cell Biology · doi:10.1186/1471-2121-14-43 · Cited by 57 · see citing works
Walker, E. C., Johnson, R. W., Hu, Y., Brennan, H. J., Poulton, I. J., Zhang, J., Jenkins, B. J., Smyth, G. K., Nicola, N. A., & Sims, N. A. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m116.748483 · Cited by 57 · see citing works
Self-citationStatistical approaches for the analysis of DNA methylation microarray data
Siegmund, K. D. (2011) · Human Genetics · doi:10.1007/s00439-011-0993-x · Cited by 56 · see citing works
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 · see citing works
Self-citationFutrega, K., Atkinson, K., Lott, W. B., & Doran, M. R. (2017) · Tissue Engineering Part C Methods · doi:10.1089/ten.tec.2016.0329 · Cited by 53 · see citing works
Damas, F., Ugrinowitsch, C., Libardi, C. A., Jannig, P. R., Hector, A. J., McGlory, C., Lixandrão, M. E., VECHIN, F. C., Montenegro, H., Tricoli, V., et al. (2018) · European Journal of Applied Physiology · doi:10.1007/s00421-018-3984-y · Cited by 50 · see citing works
Peripheral Blood MCEMP1 Gene Expression as a Biomarker for Stroke Prognosis
Raman, K., O'Donnell, M. J., Członkowska, A., Duarte, Y., López-Jaramillo, P. A., Peñaherrera, E., Sharma, M., Shoamanesh, A., Skowrońska, M., Yusuf, S., et al. (2016) · Stroke · doi:10.1161/strokeaha.115.011854 · Cited by 49 · see citing works
Schmitt, K., Molfenter, B., Laureano, N. K., Tawk, B., Bieg, M., Hostench, X. P., Weichenhan, D., Ullrich, N. D., Shang, V., Richter, D., et al. (2019) · International Journal of Cancer · doi:10.1002/ijc.32481 · Cited by 48 · see citing works
Kitchen, R. R., Sabine, V. S., Simen, A. A., Dixon, J. M., Bartlett, J. M. S., & Sims, A. H. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-589 · Cited by 47 · see citing works
Cystatin SN Upregulation in Patients with Seasonal Allergic Rhinitis
Imoto, Y., Tokunaga, T., Matsumoto, Y., Hamada, Y., Ono, M., Yamada, T., Ito, Y., Arinami, T., Okano, M., Noguchi, E., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067057 · Cited by 47 · see citing works
Under salt stress guard cells rewire ion transport and abscisic acid signaling
Karimi, S. M., Freund, M., Wager, B. M., Knoblauch, M., Fromm, J. H., Mueller, H. M., Ache, P., Krischke, M., Mueller, M. J., Müller, T., et al. (2021) · New Phytologist · doi:10.1111/nph.17376 · Cited by 46 · see citing works
Genome-Wide Identification of Expression Quantitative Trait Loci (eQTLs) in Human Heart
Koopmann, T. T., Adriaens, M. E., Moerland, P. D., Marsman, R. F. J., Westerveld, M., Lal, S., Zhang, T., Simmons, C. Q., Baczkó, I., dos Remedios, C. G., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097380 · Cited by 46 · see citing works
Increased β-cell proliferation before immune cell invasion prevents progression of type 1 diabetes
Dirice, E., Kahraman, S., Jesus, D. F. D., Ouaamari, A. E., Basile, G., Baker, R. L., Yigit, B., Piehowski, P., Kim, M., Dwyer, A. J., et al. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0061-8 · Cited by 46 · see citing works
Marsay, L., Matsumiya, M., Tanner, R., Poyntz, H., Griffiths, K., Stylianou, E., Marsh, P. D., Williams, A., Sharpe, S. A., Fletcher, H. A., et al. (2013) · Tuberculosis · doi:10.1016/j.tube.2013.04.007 · Cited by 45 · see citing works
STAT5 Activation in the Dermal Papilla Is Important for Hair Follicle Growth Phase Induction
Legrand, J. M. D., Roy, E., Ellis, J. J., François, M., Brooks, A. J., & Khosrotehrani, K. (2016) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2016.04.014 · Cited by 45 · see citing works
Human induced pluripotent stem cell derived neurons as a model for Williams-Beuren syndrome
Khattak, S., Brimble, E., Zhang, W., Zaslavsky, K., Strong, E., Ross, P. J., Hendry, J. A., Mital, S. R., Salter, M. W., Osborne, L. R., et al. (2015) · Molecular Brain · doi:10.1186/s13041-015-0168-0 · Cited by 42 · see citing works
A comprehensive database for integrated analysis of omics data in autoimmune diseases
Martorell‐Marugán, J., López-Domínguez, R., García-Moreno, A., Dominguez, D. T., Villatoro-García, J. A., Barturen, G., Martín-Gómez, A., Troulé, K., Goméz-López, G., Al‐Shahrour, F., et al. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04268-4 · Cited by 44 · see citing works
Romay, M. C., Che, N., Becker, S. N., Pouldar, D., Hagopian, R., Xiao, X. G., Lusis, A. J., Berliner, J. A., & Civelek, M. (2014) · Journal of Lipid Research · doi:10.1194/jlr.m052670 · Cited by 44 · see citing works
Gene‐expression analysis of early‐ and late‐maturation‐stage rat enamel organ
Lacruz, R. S., Smith, C. E., Chen, Y., Hubbard, M. J., Hacia, J. G., & Paine, M. L. (2011) · European Journal Of Oral Sciences · doi:10.1111/j.1600-0722.2011.00881.x · Cited by 43 · see citing works
Epigenetic Regulator Smchd1 Functions as a Tumor Suppressor
Leong, H. S., Chen, K., Hu, Y., Lee, S. C.-W., Corbin, J. E., Pakusch, M., Murphy, J. M., Majewski, I. J., Smyth, G. K., Alexander, W. S., et al. (2012) · Cancer Research · doi:10.1158/0008-5472.can-12-3019 · Cited by 43 · see citing works
Self-citationHamers, A. A. J., Argmann, C., Moerland, P. D., Koenis, D. S., Marinković, G., Sokolović, M., de Vos, A. F., de Vries, C. J. M., & van Tiel, C. M. (2016) · BMC Genomics · doi:10.1186/s12864-016-2469-9 · Cited by 43 · see citing works
Deane, C. S., Willis, C. R. G., Phillips, B. E., Atherton, P. J., Harries, L. W., Ames, R. M., Szewczyk, N. J., & Etheridge, T. (2021) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12706 · Cited by 40 · see citing works
Wentworth, J. M., Naselli, G., Ngui, K., Smyth, G. K., Liu, R., O’Brien, P. E., Bruce, C. R., Weir, J. M., Cinel, M. A., Meikle, P. J., et al. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.223 · Cited by 42 · see citing works
Self-citationSmall-molecule and CRISPR screening converge to reveal RTK dependencies in pediatric rhabdoid tumors
Oberlick, E. M., Rees, M., Seashore-Ludlow, B., Vazquez, F., Nelson, G. M., Dharia, N., Weir, B., Tsherniak, A., Ghandi, M., Krill-Burger, J., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.021 · Cited by 40 · see citing works
Transcriptome analysis reveals novel genes and immune networks dysregulated in veterans with PTSD
Mehta, D., Voisey, J., Bruenig, D., Harvey, W., Morris, C. P., Lawford, B. R., & Young, R. M. (2018) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2018.08.014 · Cited by 41 · see citing works
Kok, C. H., Yeung, D. T. O., Liu, L., Watkins, D. B., Leclercq, T. M., Dang, P., Saunders, V. A., Reynolds, J., White, D. L., & Hughes, T. P. (2019) · Blood Advances · doi:10.1182/bloodadvances.2019000195 · Cited by 40 · see citing works
Robertson, M. J., Soibam, B., O’Leary, J. G., Sampaio, L. C., & Taylor, D. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191892 · Cited by 38 · see citing works
Miyano, M., Sayaman, R. W., Stoiber, M. H., Lin, C., Stampfer, M. R., Brown, J., & LaBarge, M. A. (2017) · Aging · doi:10.18632/aging.101298 · Cited by 36 · see citing works
Shen, S., Bai, J., Wei, Y., Wang, G., Li, Q., Zhang, R., Duan, W., Yang, S., Du, M., Zhao, Y., et al. (2017) · Oncology Reports · doi:10.3892/or.2017.6057 · Cited by 36 · see citing works
Regulation of mycobacterial infection by macrophage Gch1 and tetrahydrobiopterin
McNeill, E., Stylianou, E., Crabtree, M. J., Harrington-Kandt, R., Kolb, A.-L., Diotallevi, M., Hale, A., Bettencourt, P. J. G., Tanner, R., O’Shea, M. K., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07714-9 · Cited by 35 · see citing works
Hartnell, F., Brown, A., Capone, S., Kopycinski, J. T., Bliss, C. M., Makvandi‐Nejad, S., Swadling, L., Ghaffari, E., Cicconi, P., Sorbo, M. D., et al. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2018.03175 · Cited by 36 · see citing works
Berghoff, A. S., Liao, Y., Karreman, M. A., Ilhan‐Mutlu, A., Gunkel, K., Sprick, M. R., Eisen, C., Keßler, T., Osswald, M., Wünsche, S., et al. (2020) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-20-0863 · Cited by 36 · see citing works
Potts, K. S., Sargeant, T., Dawson, C. A., Josefsson, E. C., Hilton, D. J., Alexander, W. S., & Taoudi, S. (2015) · Blood · doi:10.1182/blood-2014-12-616607 · Cited by 34 · see citing works
Ward, M. A., Pierre, J. F., Leal, R. F., Huang, Y., Shogan, B. D., Dalal, S. R., Weber, C. R., Leone, V., Musch, M. W., An, G. C., et al. (2016) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00017.2016 · Cited by 34 · see citing works
The influence of haemoglobin and iron on in vitro mycobacterial growth inhibition assays
Tanner, R., O’Shea, M. K., White, A., Müller, J., Harrington-Kandt, R., Matsumiya, M., Dennis, M. J., Parizotto, E. A., Harris, S. A., Stylianou, E., et al. (2017) · Scientific Reports · doi:10.1038/srep43478 · Cited by 34 · see citing works
Berlanga‐Taylor, A. J., Plant, K., Dahl, A., Lau, E., Hill, M., Sims, D., Heger, A., Emberson, J., Armitage, J., Clarke, R., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.04.010 · Cited by 34 · see citing works
Early Lineage Priming by Trisomy of Erg Leads to Myeloproliferation in a Down Syndrome Model
Ng, A. P., Hu, Y., Metcalf, D., Hyland, C. D., Ierino, H., Phipson, B., Wu, D., Baldwin, T. M., Kauppi, M., Kiu, H., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005211 · Cited by 33 · see citing works
Self-citationGenome-wide impact of hydrogen peroxide on maintenance DNA methylation in replicating cells
Seddon, A. R., Liau, Y., Pace, P., Miller, A. L., Das, A. B., Kennedy, M. A., Hampton, M. B., & Stevens, A. J. (2021) · Epigenetics & Chromatin · doi:10.1186/s13072-021-00388-6 · Cited by 32 · see citing works
The effect of air pollution on the transcriptomics of the immune response to respiratory infection
Croft, D. P., Burton, D. S., Nagel, D. J., Bhattacharya, S., Falsey, A. R., Georas, S. N., Hopke, P. K., Johnston, C. J., Kottmann, R. M., Litonjua, A. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98729-8 · Cited by 33 · see citing works
Divergent Influence of MicroRNA-21 Deletion on Murine Colitis Phenotypes
Wu, F., Dong, F., Arendovich, N., Zhang, J., Huang, Y., & Kwon, J. H. (2014) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000000201 · Cited by 33 · see citing works
de Masson, A., Kirilovsky, A., Zoorob, R., Avettand-Fènoël, V., Morin, V., Oudin, A., Descours, B., Rouzioux, C., & Autran, B. (2014) · AIDS · doi:10.1097/qad.0000000000000295 · Cited by 32 · see citing works
Wentworth, J. M., Zhang, J.-G., Bandala‐Sanchez, E., Naselli, G., Liu, R., Ritchie, M. E., Smyth, G. K., O'Brien, P. E., & Harrison, L. C. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.180 · Cited by 31 · see citing works
Self-citationAbrogating GPT2 in triple‐negative breast cancer inhibits tumor growth and promotes autophagy
Mitra, D., Vega-Rubín-de-Celis, S., Royla, N., Bernhardt, S., Wilhelm, H., Tarade, N., Poschet, G., Buettner, M., Binenbaum, I., Borgoni, S., et al. (2020) · International Journal of Cancer · doi:10.1002/ijc.33456 · Cited by 30 · see citing works
Norrin/Frizzled4 signalling in the preneoplastic niche blocks medulloblastoma initiation
Bassett, E. A., Tokarew, N. J. A., Allemano, E. A., Mazerolle, C. J., Morin, K., Mears, A. J., McNeill, B., Ringuette, R., Campbell, C. M., Smiley, S., et al. (2016) · eLife · doi:10.7554/elife.16764 · Cited by 30 · see citing works
Guihot, A., Luyt, C. E., Parrot, A., Rousset, D., Cavaillon, J., Boutolleau, D., Fitting, C., Pajanirassa, P., Mallet, A., Fartoukh, M. S., et al. (2014) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201311-2071oc · Cited by 29 · see citing works
HIV-1 Vpr Induces Widespread Transcriptomic Changes in CD4 + T Cells Early Postinfection
Bauby, H., Ward, C. C., Hugh-White, R., Swanson, C. M., Schulz, R., Goujon, C., & Malim, M. H. (2021) · mBio · doi:10.1128/mbio.01369-21 · Cited by 31 · see citing works
Matsumiya, M., Satti, I., Chomka, A., Harris, S. A., Stockdale, L. K., Meyer, J., Fletcher, H. A., & McShane, H. (2014) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiu615 · Cited by 29 · see citing works
- Authors
- Shi, W., Oshlack, A., & Smyth, G. K.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Shi, Wei
- Smyth, Gordon K.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-10-06
- Volume
- 38
- Issue
- 22
- Pages
- e204-e204
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3001098
- OpenAlex
- W2118373398
- Cited by
- 225
- References
- 32
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 8 · 2023: 10 · 2022: 13 · 2021: 15 · 2020: 11 · 2019: 20 · 2018: 14 · 2017: 19 · 2016: 28 · 2015: 23 · 2014: 15 · 2013: 20 · 2012: 9
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Medical Research Council
- National Health and Medical Research Council (490037)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- variance stabilizing transformation, background correction, false discovery rate, normalization, gene expression data
- MeSH terms
- Algorithms, Humans, Reproducibility of Results, Oligonucleotide Probes, Genome, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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