limma: Linear Models for Microarray Data

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482

Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829

Variance stabilization applied to microarray data calibration and to the quantification of differential expression

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

Identifying differentially expressed genes using false discovery rate controlling procedures

Reiner, A., Yekutieli, D., & Benjamini, Y. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btf877 · Cited by 1,765

Use of within-array replicate spots for assessing differential expression in microarray experiments

Smyth, G. K., Michaud, J., & Scott, H. S. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti270 · Cited by 1,406

Analysis of Messy Data, Volume I: Designed Experiments

Schmee, J., Milliken, G. A., & Johnson, D. E. (1985) · Technometrics · doi:10.2307/1270212 · Cited by 922

Statistical Issues in cDNA Microarray Data Analysis

Smyth, G. K., Yang, J. Y. H., & Speed, T. P. (2003) · Humana Press eBooks · doi:10.1385/1-59259-364-x:111 · Cited by 471

Parameter estimation for the calibration and variance stabilization of microarray data

Huber, W. K., von Heydebreck, A., Sueltmann, H., Poustka, A., & Vingron, M. (2003) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1008 · Cited by 185

Design and analysis of comparative microarray experiments

Speed, T. P. (2003) · doi:10.1201/9780203011232-9 · Cited by 67

The Histone Acetyltransferase GcnE (GCN5) Plays a Central Role in the Regulation of Aspergillus Asexual Development

Cánovas, D., Marcos, A. T., Gacek, A., Ramos, M. S., Gutiérrez, G., Reyes-Domínguez, Y., & Strauss, J. (2014) · Genetics · doi:10.1534/genetics.114.165688 · Cited by 75

Different mutant/wild-type p53 combinations cause a spectrum of increased invasive potential in nonmalignant immortalized human mammary epithelial cells

Junk, D. J., Vrba, L., Watts, G. S., Oshiro, M. M., Martinez, J. D., & Futscher, B. W. (2008) · Neoplasia · doi:10.1593/neo.08120 · Cited by 74

Effects of antenatal synthetic glucocorticoid on glucocorticoid receptor binding, DNA methylation, and genome-wide mRNA levels in the fetal male hippocampus

Crudo, A., Petropoulos, S., Suderman, M., Moisiadis, V. G., Kostaki, A., Hallett, M., Szyf, M., & Matthews, S. G. (2013) · Endocrinology · doi:10.1210/en.2013-1484 · Cited by 73

Phase IB Trial of the Anti‐Cancer Stem Cell DLL4‐Binding Agent Demcizumab with Pemetrexed and Carboplatin as First‐Line Treatment of Metastatic Non‐Squamous NSCLC

McKeage, M. J., Kotasek, D., Markman, B., Hidalgo, M., Millward, M. J., Jameson, M. B., Harris, D. L., Stagg, R. J., Kapoun, A. M., Xu, L., et al. (2018) · Targeted Oncology · doi:10.1007/s11523-017-0543-0 · Cited by 74

Comparative Genomics for the Elucidation of Multidrug Resistance in Candida lusitaniae

Kannan, A., Asner, S. A., Trachsel, E., Kelly, S., Parker, J., & Sanglard, D. (2019) · mBio · doi:10.1128/mbio.02512-19 · Cited by 63

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

Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes

Oshlack, A., Emslie, D., Corcoran, L. M., & Smyth, G. K. (2007) · Genome biology · doi:10.1186/gb-2007-8-1-r2 · Cited by 69

Selection of Reference Genes for qPCR- and ddPCR-Based Analyses of Gene Expression in Senescing Barley Leaves

Zmienko, A., Samelak-Czajka, A., Goralski, M., Sobieszczuk-Nowicka, E., Kozlowski, P., & Figlerowicz, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118226 · Cited by 62

Efficacy of the highly selective focal adhesion kinase inhibitor BI 853520 in adenocarcinoma xenograft models is linked to a mesenchymal tumor phenotype

Hirt, U. A., Waizenegger, I. C., Schweifer, N., Haslinger, C., Gerlach, D., Braunger, J., Weyer-Czernilofsky, U., Stadtmüller, H., Sapountzis, I., Bader, G., et al. (2018) · Oncogenesis · doi:10.1038/s41389-018-0032-z · Cited by 63

SIRT1 limits adipocyte hyperplasia through c-Myc inhibition

Abdesselem, H., Madani, A., Hani, A., Al-Noubi, M., Goswami, N., Ben Hamidane, H., Billing, A. M., Pasquier, J., Bonkowski, M. S., Halabi, N., et al. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.675645 · Cited by 56

A Common Variant of IL-6R is Associated with Elevated IL-6 Pathway Activity in Alzheimer’s Disease Brains

Haddick, P. C. G., Alzheimer’s Disease Genetic Consortium (ADGC), A. D. N. I. (., Larson, J. L., Rathore, N., Bhangale, T. R., Phung, Q. T., Srinivasan, K., Hansen, D. V., Lill, J. R., Pericak-Vance, M. A., et al. (2017) · Journal of Alzheimer's Disease · doi:10.3233/jad-160524 · Cited by 55

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

Intestinal Inflammation Modulates Expression of the Iron-Regulating Hormone Hepcidin Depending on Erythropoietic Activity and the Commensal Microbiota

Shanmugam, N. K. N., Trebicka, E., Fu, L.-L., Shi, H. N., & Cherayil, B. J. (2014) · The Journal of Immunology · doi:10.4049/jimmunol.1400278 · Cited by 49

Identification and Validation of Genetic Variants that Influence Transcription Factor and Cell Signaling Protein Levels

Hause, R. J., Stark, A. L., Antao, N. N., Gorsic, L. K., Chung, S. H., Brown, C. D., Wong, S. S., Gill, D. F., Myers, J. L., To, L. A., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.07.005 · Cited by 43

Profiling of Selected MicroRNAs in Proliferative Eutopic Endometrium of Women with Ovarian Endometriosis

Laudanski, P., Charkiewicz, R., Tolwinska, A., Szamatowicz, J., Charkiewicz, A., & Niklinski, J. (2015) · BioMed Research International · doi:10.1155/2015/760698 · Cited by 48

A Composite Gene Expression Signature Optimizes Prediction of Colorectal Cancer Metastasis and Outcome

Schell, M. J., Yang, M., Missiaglia, E., Delorenzi, M., Soneson, C., Yue, B., Nebozhyn, M. V., Loboda, A., Bloom, G., & Yeatman, T. J. (2016) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-15-0143 · Cited by 42

RANK links senescence to stemness in the mammary epithelia, delaying tumor onset but increasing tumor aggressiveness

Benítez, S., Cordero, A., Santamaría, P. G., Redondo-Pedraza, J., Rocha, A. S., Collado-Solé, A., Jimenez, M., Sanz-Moreno, A., Yoldi, G., Santos, J. C., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.04.022 · Cited by 41

Implications of the solvent vehicles dimethylformamide and dimethylsulfoxide for establishing transcriptomic endpoints in the zebrafish embryo toxicity test

Turner, C., Sawle, A., Fenske, M., & Cossins, A. (2012) · Environmental Toxicology and Chemistry · doi:10.1002/etc.1718 · Cited by 39

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

Yamane, J., Aburatani, S., Imanishi, S., Akanuma, H., Nagano, R., Kato, T., Sone, H., Ohsako, S., & Fujibuchi, W. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw450 · Cited by 37

Generation of a neuro-specific microarray reveals novel differentially expressed noncoding RNAs in mouse models for neurodegenerative diseases

Gstir, R., Schafferer, S., Scheideler, M., Misslinger, M., Griehl, M., Daschil, N., Humpel, C., Obermair, G. J., Schmuckermair, C., Striessnig, J., et al. (2014) · RNA · doi:10.1261/rna.047225.114 · Cited by 36

Nuclear envelope disruption triggers hallmarks of aging in lung alveolar macrophages

De Silva, N. S., Siewiera, J., Alkhoury, C., Nader, G. P. F., Nadalin, F., de Azevedo, K., Couty, M., Izquierdo, H. M., Bhargava, A., Conrad, C., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00488-w · Cited by 34

Continuous infusion of 20-hydroxyecdysone increased mass of triceps brachii in C57BL/6 mice

Cheng, D. M., Kutzler, L. W., Boler, D. D., Drnevich, J., Killefer, J., & Lila, M. A. (2013) · Phytotherapy Research · doi:10.1002/ptr.4679 · Cited by 31

How difficult is inference of mammalian causal gene regulatory networks?

Djordjevic, D., Yang, A., Zadoorian, A., Rungrugeecharoen, K., & Ho, J. W. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111661 · Cited by 29

Oxidative stress delays development and alters gene expression in the agricultural pest moth, Helicoverpa armigera

Apirajkamol, N. (., James, B., Gordon, K. H. J., Walsh, T. K., & McGaughran, A. (2020) · Ecology and Evolution · doi:10.1002/ece3.6308 · Cited by 26

Unveiling new disease, pathway, and gene associations via multi-scale neural network

Gaudelet, T., Malod-Dognin, N., Sánchez-Valle, J., Pancaldi, V., Valencia, A., & Pržulj, N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231059 · Cited by 25

Adjunct Fenofibrate Up‐regulates Bile Acid Glucuronidation and Improves Treatment Response For Patients With Cholestasis

Gallucci, G. M., Trottier, J., Hemme, C., Assis, D. N., Boyer, J. L., Barbier, O., & Ghonem, N. S. (2021) · Hepatology Communications · doi:10.1002/hep4.1787 · Cited by 26

Gene-expression data integration to squamous cell lung cancer subtypes reveals drug sensitivity

Wu, D., Pang, Y., Wilkerson, M. D., Wang, D., Hammerman, P. S., & Liu, J. S. (2013) · British Journal of Cancer · doi:10.1038/bjc.2013.452 · Cited by 25

A Strong Anti-Inflammatory Signature Revealed by Liver Transcription Profiling of Tmprss6-/- Mice

Riba, M., Rausa, M., Sorosina, M., Cittaro, D., Garcia Manteiga, J. M., Nai, A., Pagani, A., Martinelli-Boneschi, F., Stupka, E., Camaschella, C., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069694 · Cited by 23

Molecular profiling of sponge deflation reveals an ancient relaxant-inflammatory response

Ruperti, F., Becher, I., Stokkermans, A., Wang, L., Marschlich, N., Potel, C., Maus, E., Stein, F., Drotleff, B., Schippers, K. J., et al. (2024) · Current Biology · doi:10.1016/j.cub.2023.12.021 · Cited by 23

Phagocytosis of Aspergillus fumigatus by Human Bronchial Epithelial Cells Is Mediated by the Arp2/3 Complex and WIPF2

Culibrk, L., Croft, C. A., Toor, A., Yang, S. J., Singhera, G. K., Dorscheid, D. R., Moore, M. M., & Tebbutt, S. J. (2019) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2019.00016 · Cited by 20

JDSNMF: Joint Deep Semi-Non-Negative Matrix Factorization for Learning Integrative Representation of Molecular Signals in Alzheimer’s Disease

Moon, S., & Lee, H. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11080686 · Cited by 19

Specific immune modulation of experimental colitis drives enteric alpha-synuclein accumulation and triggers age-related Parkinson-like brain pathology

Grathwohl, S., Quansah, E., Maroof, N., Steiner, J. A., Spycher, L., Benmansour, F., Duran-Pacheco, G., Siebourg-Polster, J., Oroszlan-Szovik, K., Remy, H., et al. (2020) · doi:10.21203/rs.3.rs-100199/v1 · Cited by 17

Differential expression and detection of transcripts in sweetpotato (Ipomoea batatas (L.) Lam.) using cDNA microarrays

McGregor, C. (2022) · doi:10.31390/gradschool_dissertations.3754 · Cited by 15

Osteosarcoma neutrophil extracellular trap network-associated gene recurrence and metastasis model

Tang, H., Xie, J., Du, Y.-X., Tan, Z.-J., & Liang, Z.-T. (2024) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-05577-2 · Cited by 14

The impact of quantitative optimization of hybridization conditions on gene expression analysis

Sykacek, P., Kreil, D. P., Meadows, L. A., Auburn, R. P., Fischer, B., Russell, S., & Micklem, G. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-73 · Cited by 13

Bioinformatics analysis of microarray data to reveal the pathogenesis of brain ischemia

He, J., Gao, Y., Wu, G., Lei, X., Zhang, Y., Pan, W., & Yu, H. (2018) · Molecular Medicine Reports · doi:10.3892/mmr.2018.9000 · Cited by 11

Genomic and functional analyses unveil the response to hyphal wall stress in Candida albicans cells lacking β(1,3)-glucan remodeling

Degani, G., Ragni, E., Botias, P., Ravasio, D., Calderon, J., Pianezzola, E., Rodriguez-Peña, J. M., Vanoni, M. A., Arroyo, J., Fonzi, W. A., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-2853-5 · Cited by 10

Controlling for false discoveries subsequently to large scale one‐way ANOVA testing in proteomics: Practical considerations

Burger, T. (2023) · PROTEOMICS · doi:10.1002/pmic.202200406 · Cited by 10

Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition

Burtscher, M. L., Gade, S., Garrido-Rodriguez, M., Rutkowska, A., Werner, T., Eberl, H. C., Petretich, M., Knopf, N., Zirngibl, K., Grandi, P., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00025-w · Cited by 7

Rapid changes in plasma corticosterone and medial amygdala transcriptome profiles during social status change reveal molecular pathways associated with a major life history transition in mouse dominance hierarchies

Milewski, T. M., Lee, W., Young, R. L., Hofmann, H. A., & Curley, J. P. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011548 · Cited by 7

Limma: linear models for microarray data. In Bioinformatics and computational biology solutions using R and Bioconductor

Smyth, G. K. (2013)

limmaGUI: A graphical user interface for linear modeling of microarray data

Wettenhall, J. M., & Smyth, G. K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth449 · Cited by 720

Empirical array quality weights in the analysis of microarray data

Ritchie, M. E., Diyagama, D., Neilson, J., van Laar, R., Dobrovic, A., Holloway, A., & Smyth, G. K. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-261 · Cited by 312

Examples

Yes, L. (2010)

LLAMA: AN ONLINE MICROARRAY LINEAR ANALYSIS TOOLPreprint

Routley, B. S., & Muldoon, M. R. (2005) · MIMS EPrints (University of Southampton)