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Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Open accessAbstract
It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce "surrogate variable analysis" (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.
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“Surrogate variable analyses (SVAs) revealed 4 significant SVA vectors that were used as covariates in the model to correct for technical artefacts and hidden confounds (Leek & Storey, 2007).”
From DNA methylation of NR3C1 and FKBP5 is associated with posttraumatic stress disorder, posttraumatic growth, and resilience. (2020) · cites this paper as Leek & Storey, 2007
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“We used surrogate variable analysis (SVA) and 26 loosely described “biologic clusters” of tissues and cells to clearly demonstrate that, after adjusting for surrogate variables, similar tissue and cell types clustered together across experiments (Supplemental Fig. S3; Leek and Storey 2007).”
From Toward the human cellular microRNAome (2017) · cites this paper as Leek and Storey 2007
In the citing paper
“Some methods (specifically sva and PEER) that leveraged all data across all sites were quite successful at ameliorating the high rate of false positives (Fig. 1, Supplementary Fig. 2), removing 85.1% to 87.7% of the original total false positive DEGs.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [27]
In the citing paper
“However, the majority of RNA-seq false positives (>85%) could successfully be removed by subtracting the effects of latent variables identified by either sva26,27 or PEER30, which could be achieved by jointly analyzing the set of measurements of all genes across multiple sites, without a decrease in the sensitivity or specificity of DEG detection at each site or across sites.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [27]
In the citing paper
“We used surrogate variable correction in our analyses to account for such unmeasured confounders (43), which is a standard approach (44).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [43]
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Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Self-citationBrown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
A Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603
Self-citationLamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
Testing Statistical Hypotheses
Niewiadomska-Bugaj, M., & Bartoszynski, R. (2021) · Wiley series in probability and statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,188
Exploring the metabolic and genetic control of gene expression on a genomic scale.
Derisi, J., Iyer, V., & Brown, P. (1997) · Science · doi:10.1126/science.278.5338.680 · Cited by 4,832
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., & Brown, P. O. (2000) · Molecular Biology of the Cell · doi:10.1091/mbc.11.12.4241 · Cited by 4,815
Flagged first, then by citations
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- MardiaKVKentJTBibbyJM 1980 Multivariate analysis London Academic Press
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Sorted by citations
Items 901–999 of 999
Panousis, N. I., Garwany, O. E., Knights, A. J., Rop, J., Kumasaka, N., Imaz, M., Vilarino, L. B., Tsingene, A., Tokolyi, A., Barnett, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61670-9 · Cited by 15
Li, H., Zheng, T., Chen, B., Hong, G., Zhang, W., Shi, T., Li, S., Ao, L., Wang, C., & Guo, Z. (2014) · British Journal of Cancer · doi:10.1038/bjc.2014.347 · Cited by 16
Whole Blood Gene Expression Profiles of Patients with a Past Aneurysmal Subarachnoid Hemorrhage
Hof, F. N. G. V. ’., Ruigrok, Y. M., Medic, J., Sanjabi, B., van der Vlies, P., Rinkel, G. J. E., & Veldink, J. H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139352 · Cited by 16
Chawla, S., Samydurai, S., Kong, S. L., Wu, Z., Wang, Z., Tam, W. L., Sengupta, D., & Kumar, V. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkab018 · Cited by 20
Transcriptomic analysis of cardiac gene expression across the life course in male and female mice
Yusifov, A., Chhatre, V. E., Koplin, E. K., Wilson, C. E., Schmitt, E. E., Woulfe, K. C., & Bruns, D. R. (2021) · Physiological Reports · doi:10.14814/phy2.14940 · Cited by 14
Li, G., Chen, G., Liu, J., Tang, D., Zheng, J., Luo, J., Jin, M.-H., Lu, H.-S., Bao, C., Tian, J. H., et al. (2022) · BMC Pulmonary Medicine · doi:10.1186/s12890-022-02036-5 · Cited by 16
Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data
Qin, Z. S., Li, B., Conneely, K. N., Wu, H., Hu, M., Ayyala, D. N., Park, Y., Jin, V. X., Zhang, F., Zhang, H., et al. (2016) · Statistics in Biosciences · doi:10.1007/s12561-016-9145-0 · Cited by 16
Fruit and Juice Epigenetic Signatures Are Associated with Independent Immunoregulatory Pathways
Nicodemus‐Johnson, J., & Sinnott, R. (2017) · Nutrients · doi:10.3390/nu9070752 · Cited by 18
Altered DNA methylation associated with a translocation linked to major mental illness
McCartney, D. L., Walker, R. M., Morris, S. W., Anderson, S. M., Duff, B. J., Marioni, R. E., Millar, J. K., McCarthy, S., Ryan, N., Lawrie, S. M., et al. (2018) · Schizophrenia · doi:10.1038/s41537-018-0047-7 · Cited by 16
A Model Selection Approach for Expression Quantitative Trait Loci (eQTL) Mapping
Wang, P., Dawson, J. A., Keller, M. P., Yandell, B. S., Thornberry, N. A., Zhang, B. B., Wang, I., Schadt, E. E., Attie, A., & Kendziorski, C. M. (2010) · Genetics · doi:10.1534/genetics.110.122796 · Cited by 16
Highly conserved transcriptional responses to mechanical ventilation of the lung
Kompass, K. S., Deslée, G., Moore, C. H., McCurnin, D., & Pierce, R. A. (2010) · Physiological Genomics · doi:10.1152/physiolgenomics.00117.2009 · Cited by 16
Correcting for batch effects in case-control microbiome studiesPreprint
Gibbons, S. M., Duvallet, C., & Alm, E. J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/165910 · Cited by 16
Muñoz, V. R., Moreau, F., Soto, M., Watanabe, Y., Pham, L. D., Zhong, J., Zimmerman, S., Brandão, B. B., Girdhar, K., Ávila-Pacheco, J., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.08.005 · Cited by 18
Barnicle, A., Seoighe, C., Golden, A., Greally, J. M., & Egan, L. J. (2016) · Physiological Genomics · doi:10.1152/physiolgenomics.00046.2015 · Cited by 15
Wang, M., Jiang, L., Jian, R., Chan, J., Liu, Q., Snyder, M. P., & Tang, H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa904 · Cited by 15
Epigenetic age and lung cancer risk in the CLUE II prospective cohort study
Michaud, D. S., Chung, M., Zhao, N., Koestler, D. C., Lu, J., Platz, E. A., & Kelsey, K. T. (2023) · Aging · doi:10.18632/aging.204501 · Cited by 14
RELIEF: A structured multivariate approach for removal of latent inter-scanner effects
Zhang, R., Oliver, L. D., Voineskos, A. N., & Park, J. Y. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00011 · Cited by 16
The TP53-activated E3 ligase RNF144B is a tumour suppressor that prevents genomic instability
Abad, E., Sandoz, J., Romero, G., Zadra, I., Urgel-Solas, J., Borredat, P., Kourtis, S., Ortet, L., Martínez, C. M., Weghorn, D., et al. (2024) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-024-03045-4 · Cited by 14
A brief guide to analyzing expression quantitative trait loci
Ko, B. S., Lee, S. B., & Kim, T.-K. (2024) · Molecules and Cells · doi:10.1016/j.mocell.2024.100139 · Cited by 21
A pharmacogenetic study of aldehyde oxidase I in patients treated with XK469
Ramı́rez, J., Kim, T. W., Liu, W., Myers, J. L., Mirkov, S., Owzar, K., Watson, D. M., Mulkey, F. A., Gamazon, E. R., Stock, W., et al. (2013) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0000000000000023 · Cited by 15
Jiang, B., Liu, J. S., & Bulyk, M. L. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt152 · Cited by 17
ARTS: automated randomization of multiple traits for study design
Maienschein‐Cline, M., Lei, Z., Gardeux, V., Abbasi, T. A., Machado, R. F., Gordeuk, V. R., Desai, A. A., Saraf, S. L., Bahroos, N., & Lussier, Y. A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu075 · Cited by 15
Altered regulation and expression of genes by BET family of proteins in COPD patients
Malhotra, R., Kurian, N., Zhou, X.-H., Jiang, F., Monkley, S. J., DeMicco, A., Clausen, I. G., Delgren, G., Edenro, G., Ahdesmaki, M. J., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173115 · Cited by 16
MethCNA: a database for integrating genomic and epigenomic data in human cancer
Deng, G., Yang, J., Zhang, Q., Xiao, Z. J., & Cai, H. (2018) · BMC Genomics · doi:10.1186/s12864-018-4525-0 · Cited by 15
Screening as a strategy to drive regenerative medicine research
Vermeulen, S., & de Boer, J. (2020) · Methods · doi:10.1016/j.ymeth.2020.04.004 · Cited by 14
Baguma, R., Mbandi, S. K., Rodo, M., Erasmus, M., Day, J., Makhethe, L., de Kock, M., van Rooyen, M., Stone, L., Bilek, N., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.639965 · Cited by 18
Schaack, D., Weigand, M. A., & Uhle, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251800 · Cited by 16
Zhang, Y., & Liu, C. (2022) · Epigenomics · doi:10.2217/epi-2021-0190 · Cited by 20
Karantanos, T., Teodorescu, P., Arvanitis, M., Perkins, B. N., Jain, T., Dezern, A. E., Dalton, W. B., Christodoulou, I., Paun, B. C., Varadhan, R., et al. (2022) · Haematologica · doi:10.3324/haematol.2022.281444 · Cited by 15
The importance of batch sensitization in missing value imputation
Hui, H. W. H., Kong, W., Peng, H., & Goh, W. W. B. (2023) · Scientific Reports · doi:10.1038/s41598-023-30084-2 · Cited by 16
Interactions of human truncated DISC1 proteins: implications for schizophrenia
Newburn, E. N., Hyde, T. M., Ye, T., Morita, Y., Weinberger, D. R., Kleinman, J. E., & Lipska, B. K. (2011) · Translational Psychiatry · doi:10.1038/tp.2011.31 · Cited by 15
Wiewel, M. A., Scicluna, B. P., van Vught, L. A., Hoogendijk, A. J., Zwinderman, A. H., Lutter, R., Horn, J., Cremer, O. L., Bonten, M. J. M., Schultz, M. J., et al. (2018) · Annals of Intensive Care · doi:10.1186/s13613-017-0349-3 · Cited by 15
Muscle Gene Sets: a versatile methodological aid to functional genomics in the neuromuscular field
Malatras, A., Duguez, S., & Duddy, W. J. (2019) · Skeletal Muscle · doi:10.1186/s13395-019-0196-z · Cited by 16
Cross-species functional modules link proteostasis to human normal aging
Komljenović, A., Li, H., Sorrentino, V., Kutalik, Z., Auwerx, J. H., & Robinson‐Rechavi, M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007162 · Cited by 19
Bai, J., Zhang, X., Kang, X., Jin, L., Wang, P., & Wang, Z. (2019) · Oncology Letters · doi:10.3892/ol.2019.10979 · Cited by 15
Shah, R. D., Frot, B., Thanei, G.-A., & Meinshausen, N. (2020) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12359 · Cited by 16
Recovering Model Structures from Large Low Rank and Sparse Covariance Matrix EstimationPreprint
Luo, X. Q. (2011) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1111.1133 · Cited by 15
Comparative analysis of integrative classification methods for multi-omics data
Novoloaca, A., Broc, C., Belœil, L., Yu, W., & Becker, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae331 · Cited by 16
Tang, Y.-L., Li, G., Li, D., Tang, D., Huang, J.-Z., Feng, H., He, R., Huang, Z., Dang, Y., Kong, J., et al. (2022) · BMC Pulmonary Medicine · doi:10.1186/s12890-022-02095-8 · Cited by 14
Monnier, L., & Cournède, P.-H. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011880 · Cited by 14
Patient derived tumoroids of high grade neuroendocrine neoplasms for more personalized therapies
April-Monn, S., Kirchner, P., Detjen, K. M., Bräutigam, K., Trippel, M., Grob, T. J., Statzer, C., Maire, R., Kollár, A., Chouchane, A., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00549-2 · Cited by 17
Shah, S. N. A., & Parveen, R. (2025) · Biomarkers · doi:10.1080/1354750x.2025.2461698 · Cited by 14
An empirical Bayes approach for multiple tissue eQTL analysis
Wright, F. A., Rusyn, I., Shabalin, A. A., Nobel, A. B., & Li, G. (2024) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/6rbf-cv28 · Cited by 14
Mapping eQTL Networks with Mixed Graphical Markov Models
Tur, I., Roverato, A., & Castelo, R. (2014) · Genetics · doi:10.1534/genetics.114.169573 · Cited by 14
Causeur, D., Friguet, C., Houée-Bigot, M., & Kloareg, M. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i14 · Cited by 14
Detecting regulatory gene–environment interactions with unmeasured environmental factors
Fusi, N., Lippert, C., Borgwardt, K. M., Lawrence, N. D., & Stegle, O. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt148 · Cited by 14
Association of functional genetic variation in PP2A with prefrontal working memory processing
Rampino, A., Carlo, P. D., Fazio, L., Ursini, G., Pergola, G., Virgilio, C. D., Gadaleta, G., Giordano, G. M., Bertolino, A., & Blasi, G. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.08.054 · Cited by 14
Quantile normalization for combining gene-expression datasets
Pan, M., & Zhang, J. (2018) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2017.1419376 · Cited by 12
Ng’oma, E., Williams‐Simon, P. A., Rahman, A., & King, E. G. (2020) · BMC Genomics · doi:10.1186/s12864-020-6467-6 · Cited by 17
Peng, C., Heng, Y. J., Lu, D., DuPre, N. C., Kensler, K. H., Glass, K., Zeleznik, O. A., Kraft, P., Feldman, D., Hankinson, S. E., et al. (2020) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-1217 · Cited by 14
Seasonally variant gene expression in full‐term human placenta
Clarkson-Townsend, D. A., Kennedy, E. M., Everson, T. M., Deyssenroth, M. A., Burt, A., Hao, K., Chen, J., Pardue, M. T., & Marsit, C. J. (2020) · The FASEB Journal · doi:10.1096/fj.202000291r · Cited by 14
Massaro, A. N., Bammler, T. K., MacDonald, J. W., Perez, K., Comstock, B. A., & Juul, S. E. (2021) · Scientific Reports · doi:10.1038/s41598-021-84214-9 · Cited by 14
Azevedo, T., Dimitri, G. M., Lió, P., & Gamazon, E. R. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00186-6 · Cited by 14
Huang, W.-J., He, W.-Y., Li, J., He, R., Huang, Z., Zhou, X., Li, J., Zeng, D.-T., Chen, J.-T., Wu, W.-Z., et al. (2021) · Bioengineered · doi:10.1080/21655979.2021.1952791 · Cited by 14
Epigenome-wide association study of mitochondrial genome copy number
Wang, P., Castellani, C. A., Yao, J., Huan, T., Bielak, L. F., Zhao, W., Haessler, J., Joehanes, R., Sun, X., Guo, X., et al. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab240 · Cited by 12
Lecorguillé, M., Charles, M.-A. A., Lepeule, J., Lioret, S., de Lauzon‐Guillain, B., Forhan, A., Tost, J., Suderman, M. J., & Heude, B. (2021) · Epigenetics · doi:10.1080/15592294.2021.1957575 · Cited by 16
A robust mean and variance test with application to high-dimensional phenotypes
Staley, J. R., Windmeijer, F., Suderman, M. J., Lyon, M., Smith, G. D., & Tilling, K. M. (2021) · European Journal of Epidemiology · doi:10.1007/s10654-021-00805-w · Cited by 15
Wang, Y., Selvaraj, M. S., Li, X., Li, Z., Holdcraft, J. A., Arnett, D. K., Bis, J. C., Blangero, J., Boerwinkle, E., Bowden, D. W., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.09.003 · Cited by 14
A statistical approach to selecting and confirming validation targets in -omics experiments
Leek, J. T., Taub, M. A., & Rasgon, J. L. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-150 · Cited by 14
Self-citationQiu, X., Hu, R., & Wu, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099380 · Cited by 14
Fast and robust group-wise eQTL mapping using sparse graphical models
Cheng, W. D., Shi, Y., Zhang, X., & Wang, W. (2015) · BMC Bioinformatics · doi:10.1186/s12859-014-0421-z · Cited by 14
Empirical Bayes shrinkage and false discovery rate estimation, allowing for unwanted variation
Gerard, D., & Stephens, M. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy029 · Cited by 20
Naturalgwas: An R package for evaluating genomewide association methods with empirical data
François, O., & Caye, K. (2018) · Molecular Ecology Resources · doi:10.1111/1755-0998.12892 · Cited by 13
Mannheimer, J., Duval, D. L., Prasad, A., & Gustafson, D. L. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0519-2 · Cited by 14
Projected t-SNE for batch correction
Aliverti, E., Tilson, J. L., Filer, D. L., Babcock, B. R., Colaneri, A., Ocasio, J. K., Gershon, T. R., Wilhelmsen, K. C., & Dunson, D. B. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa189 · Cited by 13
Early-Life Body Adiposity and the Breast Tumor Transcriptome
Wang, J., Peng, C., Guranich, C., Heng, Y. J., Baker, G. M., Rubadue, C. A., Glass, K., Eliassen, A. H., Tamimi, R. M., Polyák, K., et al. (2020) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djaa169 · Cited by 15
Estropausal gut microbiota transplant improves measures of ovarian function in adult mice
Kim, M., Wang, J., Pilley, S. E., Lu, R., Xu, A., Kim, Y., Liu, M., Fu, X., Booth, S. L., Mullen, P. J., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01069-3 · Cited by 15
Cui, H., Xu, L., Li, Z., Hou, K., Che, X., Liu, B., Liu, Y., & Qu, X. (2020) · Oncology Letters · doi:10.3892/ol.2020.11703 · Cited by 13
Liu, Y., Liu, N., Bai, F., & Liu, Q. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.653474 · Cited by 13
Pereverzeva, L., Uhel, F., Sengers, H. P., Cremer, O. L., Schultz, M. J., Bonten, M. J. M., Scicluna, B. P., & van der Poll, T. (2021) · Annals of Intensive Care · doi:10.1186/s13613-021-00930-5 · Cited by 13
Lu, Y.-Y., Li, M., Zhou, Q., Fang, D., Wu, R., Li, Q., Chen, L., & Su, S. (2022) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2022.115324 · Cited by 13
Yu, F. G., Hu, G., Li, L., Yu, B., & Liu, R. (2022) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2022.11295 · Cited by 13
Comparison of confound adjustment methods in the construction of gene co-expression networks
Cote, A. C., Young, H. E., & Huckins, L. M. (2022) · Genome biology · doi:10.1186/s13059-022-02606-0 · Cited by 14
Roberts, M. D., & Josephs, E. B. (2023) · Genetics · doi:10.1093/genetics/iyad074 · Cited by 13
Van, R., Alvarez, D., Mize, T. J., Gannavarapu, S., Reddy, L. C., Nasoz, F., & Han, M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05801-x · Cited by 20
Proteostasis as a fundamental principle of Tau immunotherapy
Cruz, E., Nisbet, R. M., Padmanabhan, P., Waardenberg, A. J., Graham, M. E., Nkajja, G., Tapaswi, S., Connor, B. J., Robinson, P., & Götz, J. (2024) · Brain · doi:10.1093/brain/awae254 · Cited by 15
Brown, A. A., Ding, Z., Viñuela, A., Glass, D., Parts, L., Spector, T. D., Winn, J. M., & Durbin, R. M. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.011411 · Cited by 12
Ding, C., Scicluna, B. P., Stroo, I., Yang, J., Roelofs, J. J. T. H., de Boer, O. J., de Vos, A. F., Nürnberg, P., Revenko, A. S., Crosby, J. R., et al. (2019) · The Journal of Pathology · doi:10.1002/path.5354 · Cited by 13
Terkelsen, T., Krogh, A., & Papaleo, E. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007665 · Cited by 14
Mulder, C. L., Wattimury, T. M., Jongejan, A., Korver, C. M., van Daalen, S. K. M., Struijk, R. B., Borgman, S. C. M., Wurth, Y. A., Consten, D., van Echten‐Arends, J., et al. (2020) · Human Reproduction · doi:10.1093/humrep/deaa004 · Cited by 13
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Self-citation
Metadata
- Authors
- Leek, J. T., & Storey, J. D.
- Affiliations
- University of Washington (US)
- Corresponding author
- Storey, John D
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2007-09-28
- Volume
- 3
- Issue
- 9
- Pages
- e161
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC1994707
- OpenAlex
- W2137499573
- Cited by
- 1,930
- References
- 46
- Citations by year
- 2026: 77 · 2025: 114 · 2024: 131 · 2023: 126 · 2022: 155 · 2021: 195 · 2020: 177 · 2019: 156 · 2018: 141 · 2017: 143 · 2016: 112 · 2015: 79 · 2014: 87 · 2013: 74 · 2012: 54
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG002913)
- National Institute of General Medical Sciences (U54GM062119, U54 GM2119)
- 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 › Single-cell and spatial transcriptomics
- Keywords
- surrogate variable analysis, gene expression heterogeneity, unmeasured confounding, time-course expression, reproducibility, statistical power
- MeSH terms
- Algorithms, Breast Neoplasms, Computer Simulation, Data Interpretation, Statistical, Female, Humans, Kidney, Genetic Linkage, Mutation, Saccharomyces cerevisiae, Time Factors, Reproducibility of Results, Gene Expression, Genome, Human, Linear Models, Genome, Fungal, Genetic Heterogeneity, Genes, BRCA1, Quantitative Trait, Heritable, Oligonucleotide Array Sequence Analysis, Genes, BRCA2, Epigenesis, Genetic
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