The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
Abstract
The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“the significance of the apparent differential expression was assessed by a Bayesian approach [89], adjusting the p-values to control the false discovery rate.”
From Genome-wide transcriptional changes induced by phagocytosis or growth on bacteria in Dictyostelium (2008) · cites this paper as [89]
In the citing paper
“The fact that the same linear model is fitted to each gene allows us to borrow strength between genes in order to moderate the residual variances (16).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [16]
In the citing paper
“It was an innovation of the limma package to show that exact small-sample inference could be conducted using the empirical Bayes posterior variance estimators (16).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [16]
In the citing paper
“The core component of the limma package is the ability to fit gene-wise linear models to gene expression data in order to assess DE (16).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [16]
Citations
Cites
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
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,837
Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection.
Li, C., & Wong, W. H. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.1.31 · Cited by 3,133
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440
Normalization of cDNA microarray data
Smyth, G. K., & Speed, T. (2003) · Methods · doi:10.1016/s1046-2023(03)00155-5 · Cited by 1,871
Self-citationEmpirical Bayes analysis of a microarray experiment
Efron, B., Tibshirani, R., Storey, J. D., & Tusher, V. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753382129 · Cited by 1,733
Baldi, P., & Long, A. D. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.509 · Cited by 1,615
Distributions in Statistics: Continuous Multivariate Distributions.
Fisk, P. R., Johnson, N. L., & Kotz, S. (1973) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344756 · Cited by 1,552
Analysis of Variance for Gene Expression Microarray Data
Kerr, K. F., Martin, M., & Churchill, G. A. (2000) · Journal of Computational Biology · doi:10.1089/10665270050514954 · Cited by 1,310
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models
Wolfinger, R. D., Gibson, G. C., Wolfinger, E. D., Bennett, L., Hamadeh, H. K., Bushel, P. R., Afshari, C. A., & Paules, R. S. (2001) · Journal of Computational Biology · doi:10.1089/106652701753307520 · Cited by 1,117
Cited by
Sorted by citations
Items 1,001–1,100 of 1,134
Fournier, M. L., Paulson, A., Pavelka, N., Mosley, A. L., Gaudenz, K., Bradford, W. D., Glynn, E., Li, H., Sardiu, M. E., Fleharty, B., et al. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m900415-mcp200 · Cited by 161
Jiao, Y., Widschwendter, M., & Teschendorff, A. E. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu316 · Cited by 148
Ramsdale, R., Jorissen, R. N., Li, F. Z., Al-Obaidi, S., Ward, T., Sheppard, K. E., Bukczynska, P. E., Young, R. J., Boyle, S. E., Shackleton, M., et al. (2015) · Science Signaling · doi:10.1126/scisignal.aab1111 · Cited by 143
Neuropilin-1 is a T cell memory checkpoint limiting long-term antitumor immunity
Liu, C., Somasundaram, A., Manne, S., Gocher, A. M., Szymczak-Workman, A. L., Vignali, K. M., Scott, E. N., Normolle, D. P., John Wherry, E., Lipson, E. J., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0733-2 · Cited by 151
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
Liu, S., Lin, L., Jiang, P., Wang, D., & Xing, Y. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq817 · Cited by 146
Molecular fingerprinting of the podocyte reveals novel gene and protein regulatory networks
Boerries, M., Grahammer, F., Eiselein, S., Buck, M., Meyer, C., Goedel, M., Bechtel, W., Zschiedrich, S., Pfeifer, D., Laloë, D., et al. (2013) · Kidney International · doi:10.1038/ki.2012.487 · Cited by 141
Identification of Arx transcriptional targets in the developing basal forebrain
Fulp, C. T., Cho, G., Marsh, E. D., Nasrallah, I. M., Labosky, P. A., & Golden, J. A. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn271 · Cited by 138
Hepatic circadian clock oscillators and nuclear receptors integrate microbiome-derived signals
Montagner, A., Korecka, A., Polizzi, A., Lippi, Y., Blum, Y., Canlet, C., Tremblay-Franco, M., Gautier-Stein, A., Burcelin, R., Yen, Y.-C., et al. (2016) · Scientific Reports · doi:10.1038/srep20127 · Cited by 137
qSVA framework for RNA quality correction in differential expression analysis
Jaffe, A. E., Tao, R., Norris, A. L., Kealhofer, M., Nellore, A., Shin, J. H., Kim, D., Jia, Y., Hyde, T. M., Kleinman, J. E., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617384114 · Cited by 114
Chen-Plotkin, A. S., Geser, F., Plotkin, J. B., Clark, C. M., Kwong, L. K., Yuan, W., Grossman, M., Van Deerlin, V. M., Trojanowski, J. Q., & Lee, V. M.-Y. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn023 · Cited by 135
Disayabutr, S., Kim, E. K., Cha, S.-I., Green, G., Naikawadi, R. P., Jones, K. D., Golden, J. A., Schroeder, A., Matthay, M. A., Kukreja, J., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0158367 · Cited by 136
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
Opposing roles of polycomb repressive complexes in hematopoietic stem and progenitor cells
Majewski, I. J., Ritchie, M. E., Phipson, B., Corbin, J., Pakusch, M., Ebert, A., Busslinger, M., Koseki, H., Hu, Y., Smyth, G. K., et al. (2010) · Blood · doi:10.1182/blood-2009-12-260760 · Cited by 135
Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response
Watson, E., MacNeil, L. T., Arda, H. E., Zhu, L. J., & Walhout, A. J. M. (2013) · Cell · doi:10.1016/j.cell.2013.02.050 · Cited by 134
Histone acetyltransferase Rtt109 is required for Candida albicans pathogenesis
Lopes da Rosa, J., Boyartchuk, V. L., Zhu, L. J., & Kaufman, P. D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912427107 · Cited by 132
de Wilde, J., Mohren, R., van den Berg, S., Boekschoten, M., Dijk, K. W.-V., de Groot, P., Müller, M., Mariman, E., & Smit, E. (2008) · Physiological Genomics · doi:10.1152/physiolgenomics.00219.2007 · Cited by 132
Chen, H., Liu, B., Lukas, T. J., & Neufeld, A. H. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002339 · Cited by 129
Immune Activation in Retinal Aging: A Gene Expression Study
Chen, M., Muckersie, E., Forrester, J. V., & Xu, H. (2010) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.09-5103 · Cited by 130
Rapamycin transiently induces mitochondrial remodeling to reprogram energy metabolism in old hearts
Chiao, Y. A., Kolwicz, S. C., Basisty, N., Gagnidze, A., Zhang, J., Gu, H., Djukovic, D., Beyer, R. P., Raftery, D., MacCoss, M., et al. (2016) · Aging · doi:10.18632/aging.100881 · Cited by 128
Rac2 Controls Tumor Growth, Metastasis and M1-M2 Macrophage Differentiation In Vivo
Joshi, S., Singh, A. R., Zulcic, M., Bao, L., Messer, K., Ideker, T., Dutkowski, J., & Durden, D. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095893 · Cited by 128
Generation of folliculogenic human epithelial stem cells from induced pluripotent stem cells
Yang, R., Zheng, Y., Burrows, M., Liu, S., Wei, Z., Nace, A., Guo, W., Kumar, S., Cotsarelis, G., & Xu, X. (2014) · Nature Communications · doi:10.1038/ncomms4071 · Cited by 125
Robust shifts in S100a9 expression with aging: A novel mechanism for chronic inflammation
Swindell, W. R., Johnston, A., Xing, X., Little, A., Robichaud, P., Voorhees, J. J., Fisher, G., & Gudjonsson, J. E. (2013) · Scientific Reports · doi:10.1038/srep01215 · Cited by 124
Anterior definitive endoderm from ESCs reveals a role for FGF signaling
Morrison, G. M., Oikonomopoulou, I., Migueles, R. P., Soneji, S., Livigni, A., Enver, T., & Brickman, J. M. (2008) · Cell Stem Cell · doi:10.1016/j.stem.2008.07.021 · Cited by 121
A comparison of marker gene selection methods for single-cell RNA sequencing data
Pullin, J. M., & McCarthy, D. J. (2022) · doi:10.1101/2022.05.09.490241 · Cited by 122
Laterza, C., Merlini, A., De Feo, D., Ruffini, F., Menon, R., Onorati, M., Fredrickx, E., Muzio, L., Lombardo, A., Comi, G., et al. (2013) · Nature Communications · doi:10.1038/ncomms3597 · Cited by 121
Smits, M., Mir, S. E., Nilsson, R. J. A., van der Stoop, P. M., Niers, J. M., Marquez, V. E., Cloos, J., Breakefield, X. O., Krichevsky, A. M., Noske, D. P., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016282 · Cited by 118
Goeminne, L. E., Gevaert, K., & Clement, L. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.055897 · Cited by 110
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
Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods
Dal Molin, A., Baruzzo, G., & Di Camillo, B. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00062 · Cited by 107
Brunner, P. M., He, H., Pavel, A. B., Czarnowicki, T., Lefferdink, R., Erickson, T., Canter, T., Puar, N., Rangel, S. M., Malik, K., et al. (2019) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2019.04.036 · Cited by 109
Best, S. A., Ding, S., Kersbergen, A., Dong, X., Song, J.-Y., Xie, Y., Reljic, B., Li, K., Vince, J. E., Rathi, V., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12164-y · Cited by 108
Tulstrup, M., Soerensen, M., Hansen, J. W., Gillberg, L., Needhamsen, M., Kaastrup, K., Helin, K., Christensen, K., Weischenfeldt, J., & Grønbæk, K. (2021) · Nature Communications · doi:10.1038/s41467-021-26093-2 · Cited by 108
Autoreactive CD8+ T cells are restrained by an exhaustion-like program that is maintained by LAG3
Grebinoski, S., Zhang, Q., Cillo, A. R., Manne, S., Xiao, H., Brunazzi, E. A., Tabib, T., Cardello, C., Lian, C. G., Murphy, G. F., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01210-5 · Cited by 107
Sleutels, F., Soochit, W., Bartkuhn, M., Heath, H., Dienstbach, S., Bergmaier, P., Franke, V., Rosa-Garrido, M., van de Nobelen, S., Caesar, L., et al. (2012) · Epigenetics & Chromatin · doi:10.1186/1756-8935-5-8 · Cited by 96
Cheng, P. F., Shakhova, O., Widmer, D. S., Eichhoff, O. M., Zingg, D., Frommel, S. C., Belloni, B., Raaijmakers, M. I., Goldinger, S. M., Santoro, R., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0594-4 · Cited by 100
Subtypes of familial breast tumours revealed by expression and copy number profiling
kConFab Investigators, Waddell, N., Arnold, J., Cocciardi, S., da Silva, L., Marsh, A., Riley, J., Johnstone, C. N., Orloff, M., Assie, G., et al. (2010) · Breast Cancer Research and Treatment · doi:10.1007/s10549-009-0653-1 · Cited by 99
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
Systematic Identification of the HSP90 Regulated Proteome
Wu, Z., Moghaddas Gholami, A., & Kuster, B. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.016675 · Cited by 94
Ghosh, D., Ding, L., Sivaprasad, U., Geh, E., Biagini Myers, J., Bernstein, J. A., Khurana Hershey, G. K., & Mersha, T. B. (2015) · PLOS ONE · doi:10.1371/journal.pone.0144316 · Cited by 94
van Steenoven, I., Koel-Simmelink, M. J. A., Vergouw, L. J. M., Tijms, B. M., Piersma, S. R., Pham, T. V., Bridel, C., Ferri, G.-L., Cocco, C., Noli, B., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00388-2 · Cited by 83
Zhang, L., Fang, Y., Cheng, X., Lian, Y.-J., & Xu, H.-L. (2019) · Molecular Neurobiology · doi:10.1007/s12035-018-1299-y · Cited by 88
Arwert, E. N., Milford, E. L., Rullan, A., Derzsi, S., Hooper, S., Kato, T., Mansfield, D., Melcher, A., Harrington, K. J., & Sahai, E. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0527-7 · Cited by 90
Kim, K., Shin, J., Lee, S.-H., Kweon, H.-S., Maloof, J. N., & Choi, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011066108 · Cited by 86
Rate, A., Upham, J. W., Bosco, A., McKenna, K. L., & Holt, P. G. (2009) · The Journal of Immunology · doi:10.4049/jimmunol.182.1.72 · Cited by 88
ROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer
Seyednasrollah, F., Rantanen, K., Jaakkola, P., & Elo, L. L. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv806 · Cited by 75
Complement anaphylatoxin C3a is a potent inducer of embryonic chick retina regeneration
Haynes, T., Luz-Madrigal, A., Reis, E. S., Echeverri Ruiz, N. P., Grajales-Esquivel, E., Tzekou, A., Tsonis, P. A., Lambris, J. D., & Del Rio-Tsonis, K. (2013) · Nature Communications · doi:10.1038/ncomms3312 · Cited by 87
Fassbender, A., Verbeeck, N., Börnigen, D., Kyama, C. M., Bokor, A., Vodolazkaia, A., Peeraer, K., Tomassetti, C., Meuleman, C., Gevaert, O., et al. (2012) · Human Reproduction · doi:10.1093/humrep/des127 · Cited by 87
Buhl, J. L., Selt, F., Hielscher, T., Guiho, R., Ecker, J., Sahm, F., Ridinger, J., Riehl, D., Usta, D., Ismer, B., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-1965 · Cited by 84
Comparison of methods to detect differentially expressed genes between single-cell populations
Jaakkola, M. K., Seyednasrollah, F., Mehmood, A., & Elo, L. L. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw057 · Cited by 86
zic-1 expression in planarian neoblasts after injury controls anterior pole regeneration
Vásquez-Doorman, C., & Petersen, C. P. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004452 · Cited by 83
Transcriptome of embryonic and neonatal mouse cortex by high-throughput RNA sequencing
Han, X., Wu, X., Chung, W.-Y., Li, T., Nekrutenko, A., Altman, N. S., Chen, G., & Ma, H. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0902417106 · Cited by 83
Photoreceptor-Mediated Bending towards UV-B in Arabidopsis
Vandenbussche, F., Tilbrook, K., Fierro, A. C., Marchal, K., Poelman, D., Van Der Straeten, D., & Ulm, R. (2014) · Molecular Plant · doi:10.1093/mp/ssu039 · Cited by 82
MicroRNA Profiling Reveals Marker of Motor Neuron Disease in ALS Models
Hoye, M. L., Koval, E. D., Wegener, A. J., Hyman, T. S., Yang, C., O'Brien, D. R., Miller, R. L., Cole, T., Schoch, K. M., Shen, T., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3582-16.2017 · Cited by 82
Piras, F., Riba, M., Petrillo, C., Lazarevic, D., Cuccovillo, I., Bartolaccini, S., Stupka, E., Gentner, B., Cittaro, D., Naldini, L., et al. (2017) · EMBO Molecular Medicine · doi:10.15252/emmm.201707922 · Cited by 75
cellHarmony: cell-level matching and holistic comparison of single-cell transcriptomes
DePasquale, E. A. K., Schnell, D., Dexheimer, P., Ferchen, K., Hay, S., Chetal, K., Valiente-Alandí, Í., Blaxall, B. C., Grimes, H. L., & Salomonis, N. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz789 · Cited by 67
Afshar, Y., Hastings, J., Roqueiro, D., Jeong, J.-W., Giudice, L. C., & Fazleabas, A. T. (2013) · Biology of Reproduction · doi:10.1095/biolreprod.112.104497 · Cited by 77
A conserved fertilization complex bridges sperm and egg in vertebrates
Deneke, V. E., Blaha, A., Lu, Y., Suwita, J. P., Draper, J. M., Phan, C. S., Panser, K., Schleiffer, A., Jacob, L., Humer, T., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.035 · Cited by 78
Strong, E., Butcher, D. T., Singhania, R., Mervis, C. B., Morris, C. A., De Carvalho, D., Weksberg, R., & Osborne, L. R. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.05.019 · Cited by 71
Depraetere, O., Deschoenmaeker, F., Badri, H., Monsieurs, P., Foubert, I., Leys, N., Wattiez, R., & Muylaert, K. (2015) · PLOS ONE · doi:10.1371/journal.pone.0132461 · Cited by 68
Peng, D. H., Rodriguez, B. L., Diao, L., Gaudreau, P.-O., Padhye, A., Konen, J. M., Ochieng, J. K., Class, C. A., Fradette, J. J., Gibson, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22875-w · Cited by 72
Genome-wide transcriptional changes induced by phagocytosis or growth on bacteria in Dictyostelium
Sillo, A., Bloomfield, G., Balest, A., Balbo, A., Pergolizzi, B., Peracino, B., Skelton, J., Ivens, A., & Bozzaro, S. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-291 · Cited by 69
The use of miRNA microarrays for the analysis of cancer samples with global miRNA decrease
Wu, D., Hu, Y., Tong, S., Williams, B. R. G., Smyth, G. K., & Gantier, M. P. (2013) · RNA · doi:10.1261/rna.035055.112 · Cited by 71
Self-citationKinstrie, R., Horne, G. A., Morrison, H., Irvine, D., Munje, C., Castañeda, E. G., Moka, H. A., Dunn, K., Cassels, J. E., Parry, N., et al. (2020) · Leukemia · doi:10.1038/s41375-019-0684-5 · Cited by 67
Ischemia/reperfusion induces interferon stimulated gene expression in microglia
McDonough, A., Lee, R. V., Noor, S., Lee, C., Le, T., Iorga, M., Phillips, J. L. H., Murphy, S., Möller, T., & Weinstein, J. R. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.0725-17.2017 · Cited by 71
A guide to creating design matrices for gene expression experiments
Law, C. W., Zeglinski, K., Dong, X., Alhamdoosh, M., Smyth, G. K., & Ritchie, M. E. (2020) · F1000Research · doi:10.12688/f1000research.27893.1 · Cited by 69
Self-citationPang, 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
Wirz, K. T. S., Bossers, K., Stargardt, A., Kamphuis, W., Swaab, D. F., Hol, E. M., & Verhaagen, J. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.11.008 · Cited by 65
Riedelberger, M., Penninger, P., Tscherner, M., Seifert, M., Jenull, S., Brunnhofer, C., Scheidl, B., Tsymala, I., Bourgeois, C., Petryshyn, A., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.01.023 · Cited by 64
Jokinen, J., Boström, A. E., Dadfar, A., Ciuculete, D. M., Chatzittofis, A., Åsberg, M., & Schiöth, H. B. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.018 · Cited by 67
Farkas, S. A., Vymetalkova, V., Vodickova, L., Vodicka, P., & Nilsson, T. K. (2014) · Epigenomics · doi:10.2217/epi.14.7 · Cited by 67
Notch/HES1-mediated PARP1 activation: a cell type–specific mechanism for tumor suppression
Kannan, S., Fang, W., Song, G., Mullighan, C. G., Hammitt, R., McMurray, J., & Zweidler-McKay, P. A. (2011) · Blood · doi:10.1182/blood-2009-12-253419 · Cited by 66
First-in-class MKK4 inhibitors enhance liver regeneration and prevent liver failure
Zwirner, S., Abu Rmilah, A. A., Klotz, S., Pfaffenroth, B., Kloevekorn, P., Moschopoulou, A. A., Schuette, S., Haag, M., Selig, R., Li, K., et al. (2024) · Cell · doi:10.1016/j.cell.2024.02.023 · Cited by 65
Neuroblastoma cells depend on HDAC11 for mitotic cell cycle progression and survival
Thole, T. M., Lodrini, M., Fabian, J., Wuenschel, J., Pfeil, S., Hielscher, T., Kopp-Schneider, A., Heinicke, U., Fulda, S., Witt, O., et al. (2017) · Cell Death & Disease · doi:10.1038/cddis.2017.49 · Cited by 67
Fox, A. S., Oler, J. A., Shelton, S. E., Nanda, S. A., Davidson, R. J., Roseboom, P. H., & Kalin, N. H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1206723109 · Cited by 57
Ng, I. H. W., Ng, D. C. H., Jans, D. A., & Bogoyevitch, M. A. (2012) · Biochemical Journal · doi:10.1042/bj20120941 · Cited by 63
Schmidt, K., Hughes, C., Chudek, J. A., Goodyear, S. R., Aspden, R. M., Talbot, R., Gundersen, T. E., Blomhoff, R., Henderson, C., Wolf, C. R., et al. (2009) · Molecular and Cellular Biology · doi:10.1128/mcb.01638-08 · Cited by 61
Regulation of extra-embryonic endoderm stem cell differentiation by Nodal and Cripto signaling
Julio, M. K.-D., Alvarez, M. J., Galli, A., Chu, J., Price, S. M., Califano, A., & Shen, M. M. (2011) · Development · doi:10.1242/dev.065656 · Cited by 62
The Pex1-G844D mouse: a model for mild human Zellweger spectrum disorder
Hiebler, S., Masuda, T., Hacia, J. G., Moser, A. B., Faust, P. L., Liu, A., Chowdhury, N., Huang, N., Lauer, A., Bennett, J., et al. (2014) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2014.01.008 · Cited by 60
Khulan, B., Manning, J. R., Dunbar, D. R., Seckl, J. R., Raikkonen, K., Eriksson, J. G., & Drake, A. J. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.94 · Cited by 59
Kaur, A. P., Lansky, I. B., & Wilks, A. (2009) · Journal of Biological Chemistry · doi:10.1074/jbc.m806068200 · Cited by 55
Badri, H., Monsieurs, P., Coninx, I., Wattiez, R., & Leys, N. (2015) · MicrobiologyOpen · doi:10.1002/mbo3.229 · Cited by 56
Syed, Y. A., Baer, A., Hofer, M. P., González, G. A., Rundle, J., Myrta, S., Huang, J. K., Zhao, C., Rossner, M. J., Trotter, M. W. B., et al. (2013) · EMBO Molecular Medicine · doi:10.1002/emmm.201303123 · Cited by 58
Wexler, E. M., Rosen, E., Lu, D., Osborn, G. E., Martin, E., Raybould, H., & Geschwind, D. H. (2011) · Science Signaling · doi:10.1126/scisignal.2002282 · Cited by 56
Beste, D. J. V., Laing, E., Bonde, B., Avignone-Rossa, C., Bushell, M. E., & McFadden, J. J. (2007) · Journal of Bacteriology · doi:10.1128/jb.01787-06 · Cited by 55
Coregulator control of androgen receptor action by a novel nuclear receptor-binding motif
Jehle, K., Cato, L., Neeb, A., Muhle-Goll, C., Jung, N., Smith, E. W., Buzon, V., Carbó, L. R., Estébanez-Perpiñá, E., Schmitz, K., et al. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.534859 · Cited by 53
Identification and characterization of mouse otic sensory lineage genes
Hartman, B. H., Durruthy-Durruthy, R., Laske, R. D., Losorelli, S., & Heller, S. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00079 · Cited by 49
Kurzawa, N., Becher, I., Sridharan, S., Franken, H., Mateus, A., Anders, S., Bantscheff, M., Huber, W., & Savitski, M. M. (2020) · Nature Communications · doi:10.1038/s41467-020-19529-8 · Cited by 53
Optimized CRISPR-mediated gene knockin reveals FOXP3-independent maintenance of human Treg identity
Lam, A. J., Lin, D. T. S., Gillies, J. K., Uday, P., Pesenacker, A. M., Kobor, M. S., & Levings, M. K. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109494 · Cited by 52
Davey, J. R., Watt, K. I., Parker, B. L., Chaudhuri, R., Ryall, J. G., Cunningham, L., Qian, H., Sartorelli, V., Sandri, M., Chamberlain, J., et al. (2016) · JCI Insight · doi:10.1172/jci.insight.85477 · Cited by 51
Lanz, T. A., Guilmette, E., Gosink, M. M., Fischer, J. E., Fitzgerald, L. W., Stephenson, D. T., & Pletcher, M. T. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-45 · Cited by 52
Differential gene expression in migratory streams of cortical interneurons
Antypa, M., Faux, C., Eichele, G., Parnavelas, J. G., & Andrews, W. D. (2011) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2011.07896.x · Cited by 49
Blache, U., Horton, E. R., Xia, T., Schoof, E. M., Blicher, L. H., Schönenberger, A., Snedeker, J. G., Martin, I., Erler, J. T., & Ehrbar, M. (2019) · Life Science Alliance · doi:10.26508/lsa.201900304 · Cited by 49
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
Sex specific molecular networks and key drivers of Alzheimer’s disease
Guo, L., Cao, J., Hou, J., Li, Y., Huang, M., Zhu, L., Zhang, L., Lee, Y., Duarte, M. L., Zhou, X., et al. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00624-5 · Cited by 49
Bhar, A., Haubrock, M., Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Wingender, E. (2013) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-8-9 · Cited by 44
Ciuculete, D. M., Boström, A. E., Voisin, S., Philipps, H., Titova, O. E., Bandstein, M., Nikontovic, L., Williams, M. J., Mwinyi, J., & Schiöth, H. B. (2017) · Translational Psychiatry · doi:10.1038/tp.2016.275 · Cited by 40
Viereckel, T., Dumas, S., Smith-Anttila, C. J. A., Vlcek, B., Bimpisidis, Z., Lagerström, M. C., Konradsson-Geuken, Å., & Wallén-Mackenzie, Å. (2016) · Scientific Reports · doi:10.1038/srep35203 · Cited by 45
A possible role for fumagillin in cellular damage during host infection by Aspergillus fumigatus
Guruceaga, X., Ezpeleta, G., Mayayo, E., Sueiro-Olivares, M., Abad-Diaz-De-Cerio, A., Aguirre Urízar, J. M., Liu, H. G., Wiemann, P., Bok, J. W., Filler, S. G., et al. (2018) · Virulence · doi:10.1080/21505594.2018.1526528 · Cited by 44
Metadata
- Authors
- Smyth, G. K.
- Affiliations
- Walter and Eliza Hall Institute of Medical Research (AU)
- Corresponding author
- Smyth, Gordon K
- ORCID
- Smyth, Gordon K
- Venue
- Statistical Applications in Genetics and Molecular Biology
- Publisher
- Walter de Gruyter GmbH
- Published
- 2004-01-12
- Volume
- 3
- Issue
- 1
- Pages
- 1-25
- ISSN
- 1544-6115 (ISSN-L)
- OpenAlex
- W2081098333
- Cited by
- 11,829
- References
- 30
- Citations by year
- 2026: 214 · 2025: 252 · 2024: 324 · 2023: 427 · 2022: 374 · 2021: 464 · 2020: 534 · 2019: 510 · 2018: 550 · 2017: 634 · 2016: 718 · 2015: 892 · 2014: 901 · 2013: 917 · 2012: 982
- FWCI
- 78.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistical Applications in Genetics and Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- 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
- differential gene expression, microarray experiments, empirical Bayes, hierarchical models, posterior odds, hyperparameter estimation, composite null hypothesis, two-color microarrays
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
