Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation
Open accessAbstract
A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Regularization by combining information across similar genes is commonly applied in learning error models for bulk RNA-seq in differential expression analysis, but has not been previously applied in this context for single-cell normalization.
“This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis [22–25], but to our knowledge has not been previously applied in this context for single-cell normalization.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [22]
edgeR introduced GLM algorithms and statistical methods for estimating biological variation on a genewise basis and separating it from technical variation.
“Additionally, edgeR [22] introduced GLM algorithms and statistical methods for estimating biological variation on a genewise basis and separating it from technical variation.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [22]
A regularization step that pools parameter estimates across genes with similar mean abundance can effectively overcome the overfitting and yield reproducible models.
“We demonstrate that a regularization step, a commmon step in bulk RNA-seq analysis [22, 28] where parameter estimates are pooled across genes with similar mean abundance, can effectively overcome this challenge and yield reproducible models.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [22]
The quasi-likelihood framework in edgeR was used to handle the pronounced variability of single-cell expression states.
“and the quasi-likelihood framework within edgeR to handle the pronounced variability of the single cell expression states.”
From Epigenetic regulation of brain region-specific microglia clearance activity (2018) · cites this paper as [82]
A negative binomial model in edgeR was used to account for the sparse single-cell count matrix.
“We used the negative binomial (NB) model implemented in edgeR (3.10.0)79–83 for our single cell differential expression analysis to explicitly account for the sparse count matrix”
From Epigenetic regulation of brain region-specific microglia clearance activity (2018) · cites this paper as [82]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
Self-citationA Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526 · see citing works
(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333 · see citing works
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836 · see citing works
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome Biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 13,133 · see citing works
Self-citationSmyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829 · see citing works
Self-citationIn 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 · see citing works
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876 · see citing works
Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344614 · Cited by 7,321 · see citing works
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107 · see citing works
Self-citationmRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Pan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951 · 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
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771 · see citing works
Introduction to matrix computations
Stewart, G. W. (1973) · International Linear Collider · Cited by 2,394 · see citing works
RNA sequencing: advances, challenges and opportunities
Ozsolak, F., & Milos, P. M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2934 · Cited by 2,135 · see citing works
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · see citing works
Baldi, P., & Long, A. D. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.509 · Cited by 1,622 · see citing works
Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples
White, J. R., Nagarajan, N., & Pop, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000352 · Cited by 1,581 · see citing works
Generalized linear models. 2nd ed.
McCullagh, P., & Nelder, J. (1993) · Journal of the American Statistical Association · doi:10.2307/2290358 · Cited by 1,581 · see citing works
Generalized Linear Models, 2nd Edn.
Cheek, P. J., McCullagh, P., & Nelder, J. A. (1990) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2347392 · Cited by 1,517 · see citing works
Parameter Orthogonality and Approximate Conditional Inference
Cox, D. R., & Reid, N. M. (1987) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1987.tb01422.x · Cited by 1,370 · see citing works
Sultan, M., Schulz, M. H., Richard, H., Magen, A., Klingenhoff, A., Scherf, M., Seifert, M., Borodina, T., Soldatov, A., Parkhomchuk, D., et al. (2008) · Science · doi:10.1126/science.1160342 · Cited by 1,293 · see citing works
Algorithms 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
RNA-Seq gene expression estimation with read mapping uncertainty
Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A., & Dewey, C. N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp692 · Cited by 1,198 · see citing works
Small-sample estimation of negative binomial dispersion, with applications to SAGE data
Robinson, M. D., & Smyth, G. K. (2007) · Biostatistics · doi:10.1093/biostatistics/kxm030 · Cited by 1,073 · see citing works
Self-citationStem cell transcriptome profiling via massive-scale mRNA sequencing
Cloonan, N., Forrest, A. R. R., Kolle, G., Gardiner, B. B. A., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe, A. L., Wani, S., Bethel, G., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1223 · Cited by 1,035 · cited 1× · see citing works
“One strategy for RNA-Seq data analysis is to standardize and transform the read counts to approximate normality, then analyse as for microarray data (3,6,21).”
MethodModerated statistical tests for assessing differences in tag abundance
Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm453 · Cited by 856 · see citing works
Self-citationTranscriptome genetics using second generation sequencing in a Caucasian population
Montgomery, S. B., Sammeth, M., Gutierrez-Arcelus, M., Lach, R. P., Ingle, C., Nisbett, J., Guigo, R., & Dermitzakis, E. T. (2010) · Nature · doi:10.1038/nature08903 · Cited by 797 · see citing works
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Hardcastle, T. J., & Kelly, K. A. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-422 · Cited by 755 · see citing works
't Hoen, P. A. C., Ariyurek, Y., Thygesen, H. H., Vreugdenhil, E., Vossen, R. H. A. M., de Menezes, R. X., Boer, J. M., van Ommen, G.-J. B., & den Dunnen, J. T. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn705 · Cited by 742 · see citing works
From RNA-seq reads to differential expression results
Oshlack, A., Robinson, M. D., & Young, M. D. (2010) · Genome biology · doi:10.1186/gb-2010-11-12-220 · Cited by 697 · see citing works
Encyclopedia of Biostatistics.
Kemp, C. D., Armitage, P., & Coulton, T. (1998) · Biometrics · doi:10.2307/2533871 · Cited by 674 · see citing works
Wright, G. W. J., & Simon, R. M. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg345 · Cited by 662 · see citing works
Brent, R. P. (1975) · Mathematics of Computation · doi:10.1090/s0025-5718-1975-0371062-9 · Cited by 608 · see citing works
Removing technical variability in RNA-seq data using conditional quantile normalization
Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607 · see citing works
Quantitative comparison of genome-wide DNA methylation mapping technologies
Bock, C., Tomazou, E. M., Brinkman, A. B., Müller, F., Simmer, F., Gu, H., Jäger, N., Gnirke, A., Stunnenberg, H. G., & Meissner, A. (2010) · Nature Biotechnology · doi:10.1038/nbt.1681 · Cited by 587 · see citing works
Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data.
Degner, J. F., Marioni, J. C., Pai, A. A., Pickrell, J. K., Nkadori, E., Gilad, Y., & Pritchard, J. K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp579 · Cited by 502 · see citing works
Statistical Design and Analysis of RNA Sequencing Data
Auer, P. L., & Doerge, R. W. (2010) · Genetics · doi:10.1534/genetics.110.114983 · Cited by 393 · see citing works
Cloud-scale RNA-sequencing differential expression analysis with Myrna
Langmead, B., Hansen, K. D., & Leek, J. T. (2010) · Genome biology · doi:10.1186/gb-2010-11-8-r83 · Cited by 313 · see citing works
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 · see citing works
Self-citationNext-generation tag sequencing for cancer gene expression profilingPreprint
Morrissy, A. S., Morin, R. D., Delaney, A. D., Zeng, T., McDonald, H., Jones, S. J. M., Zhao, Y., Hirst, M., & Marra, M. A. (2009) · Genome Research · doi:10.1101/gr.094482.109 · Cited by 309 · see citing works
Sex-specific and lineage-specific alternative splicing in primatesPreprint
Blekhman, R., Marioni, J. C., Zumbo, P., Stephens, M., & Gilad, Y. (2009) · Genome Research · doi:10.1101/gr.099226.109 · Cited by 266 · see citing works
Annotating genomes with massive-scale RNA sequencing
Denœud, F., Aury, J., Silva, C. D., Noël, B., Rogier, O., Delledonne, M., Morgante, M., Valle, G., Wincker, P., Scarpelli, C., et al. (2008) · Genome biology · doi:10.1186/gb-2008-9-12-r175 · Cited by 250 · see citing works
Sequencing technology does not eliminate biological variability
Hansen, K. D., Wu, Z., Irizarry, R. A., & Leek, J. T. (2011) · Nature Biotechnology · doi:10.1038/nbt.1910 · Cited by 226 · see citing works
Modeling non-uniformity in short-read rates in RNA-Seq data
Li, J., Jiang, H., & Wong, W. H. (2010) · Genome Biology · doi:10.1186/gb-2010-11-5-r50 · Cited by 193 · see citing works
A two-parameter generalized Poisson model to improve the analysis of RNA-seq data
Srivastava, S., & Chen, L. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq670 · Cited by 152 · see citing works
Tuch, B. B., Laborde, R. R., Xu, X. J., Gu, J. H., Chung, C. B., Monighetti, C. K., Stanley, S. J., Olsen, K. D., Kasperbauer, J. L., Moore, E. J., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009317 · Cited by 148 · see citing works
A Two-Stage Poisson Model for Testing RNA-Seq Data
Auer, P. L., & Doerge, R. W. (2011) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1627 · Cited by 147 · see citing works
Integrative analysis of RUNX1 downstream pathways and target genes
Michaud, J., Simpson, K., Escher, R., Buchet-Poyau, K., Beißbarth, T., Carmichael, C., Ritchie, M. E., Schütz, F., Cannon, P., Liu, M., et al. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-363 · Cited by 135 · see citing works
Self-citationRobinson, M. D., Stirzaker, C., Statham, A. L., Coolen, M. W., Song, J. Z., Nair, S. S., Strbenac, D., Speed, T. P., & Clark, S. J. (2010) · Genome Research · doi:10.1101/gr.110601.110 · Cited by 131 · see citing works
A powerful and flexible approach to the analysis of RNA sequence count data
Zhou, Y., Xia, K., & Wright, F. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr449 · Cited by 120 · see citing works
Statistical design and analysis
Kelton, W. D. (1986) · Winter Simulation Conference · doi:10.1145/318242.318259 · Cited by 97 · see citing works
Osborne, M. R. (1992) · International Statistical Review · doi:10.2307/1403504 · Cited by 95 · see citing works
Identifying differences in protein expression levels by spectral counting and feature selection
Carvalho, P. C., Hewel, J. A., Barbosa, V. C., & Yates, J. R. (2008) · Genetics and Molecular Research · doi:10.4238/vol7-2gmr426 · Cited by 92 · see citing works
Lu, J., Tomfohr, J. K., & Kepler, T. B. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-165 · Cited by 79 · see citing works
Vêncio, R. Z. N., Brentani, H., Patrão, D. F. D. C., & Pereira, C. A. D. B. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-119 · Cited by 65 · see citing works
Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates
Baggerly, K., Deng, L., Morris, J. S., & Aldaz, C. M. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-144 · Cited by 58 · see citing works
The Evolving Transcriptome of Head and Neck Squamous Cell Carcinoma: A Systematic Review
Yu, Y., Kuo, H., & Chang, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003215 · Cited by 57 · see citing works
Gene expression profiling of human breast tissue samples using SAGE-SeqPreprint
Wu, Z. J., Meyer, C. A., Choudhury, S. A., Shipitsin, M. V., Maruyama, R., Bessarabova, M., Nikolskaya, T., Sukumar, S. V., Schwartzman, A., Liu, J. S., et al. (2010) · Genome Research · doi:10.1101/gr.108217.110 · Cited by 41 · see citing works
Using credibility intervals instead of hypothesis tests in SAGE analysis
Vêncio, R. Z. N., Brentani, H., & Pereira, C. A. D. B. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg357 · Cited by 35 · see citing works
Computer Science and Applied Mathematics
(2023) · doi:10.26661/2413-6549 · Cited by 30 · see citing works
Optimization and Nonlinear Equations
Smyth, G. K. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a14027 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155 · see citing works
Self-citationHafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
Liao, Y., Smyth, G. K., & Shi, W. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt214 · Cited by 3,064 · see citing works
Self-citationGenomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma
Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., Berent-Maoz, B., Pang, J., Chmielowski, B., Cherry, G., et al. (2016) · Cell · doi:10.1016/j.cell.2016.02.065 · Cited by 3,448 · see citing works
Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
Lloyd-Price, J., Arze, C., Ananthakrishnan, A. N., Schirmer, M., Avila-Pacheco, J., Poon, T. W., Andrews, E., Ajami, N. J., Bonham, K. S., Brislawn, C. J., et al. (2019) · Nature · doi:10.1038/s41586-019-1237-9 · Cited by 3,143 · see citing works
Opportunities and challenges in long-read sequencing data analysis
Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020) · Genome Biology · doi:10.1186/s13059-020-1935-5 · Cited by 2,530 · see citing works
ComBat-seq: batch effect adjustment for RNA-seq count data
Zhang, Y., Parmigiani, G., & Johnson, W. E. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa078 · Cited by 1,566 · see citing works
Detecting differential usage of exons from RNA-seq data
Anders, S., Reyes, A., & Huber, W. (2012) · Genome Research · doi:10.1101/gr.133744.111 · Cited by 1,545 · see citing works
The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight
Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, R. C., Lin, L., Macias, B. R., McKenna, M. J., Meydan, C., Mishra, T., Nasrini, J., et al. (2019) · Science · doi:10.1126/science.aau8650 · Cited by 1,128 · see citing works
Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span
Misharin, A. V., Morales-Nebreda, L., Reyfman, P. A., Cuda, C. M., Walter, J. M., McQuattie-Pimentel, A. C., Chen, C.-I., Anekalla, K. R., Joshi, N., Williams, K. J. N., et al. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20162152 · Cited by 1,133 · see citing works
Confronting false discoveries in single-cell differential expression
Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Hudelle, R., Qaiser, T., Matson, K. J. E., Barraud, Q., et al. (2021) · doi:10.1101/2021.03.12.435024 · Cited by 1,017 · see citing works
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
L. Lun, A. T., Bach, K., & Marioni, J. C. (2016) · Genome Biology · doi:10.1186/s13059-016-0947-7 · Cited by 1,108 · see citing works
Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), Savas, P., Virassamy, B., Ye, C., Salim, A., Mintoff, C. P., Caramia, F., Salgado, R., Byrne, D. J., Teo, Z. L., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0078-7 · Cited by 992 · see citing works
Differential abundance testing on single-cell data using k-nearest neighbor graphs
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · see citing works
Single-cell multi-omics analysis of the immune response in COVID-19
Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID-19 BioResource Collaboration, Stephenson, E., Reynolds, G., Botting, R. A., Calero-Nieto, F. J., Morgan, M. D., Tuong, Z. K., Bach, K., Sungnak, W., Worlock, K. B., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01329-2 · Cited by 742 · see citing works
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r95 · Cited by 733 · see citing works
Spatial multi-omic map of human myocardial infarction
Kuppe, C., Ramirez Flores, R. O., Li, Z., Hayat, S., Levinson, R. T., Liao, X., Hannani, M. T., Tanevski, J., Wünnemann, F., Nagai, J. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05060-x · Cited by 772 · see citing works
Parhi, L., Alon-Maimon, T., Sol, A., Nejman, D., Shhadeh, A., Fainsod-Levi, T., Yajuk, O., Isaacson, B., Abed, J., Maalouf, N., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16967-2 · Cited by 716 · see citing works
High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy
Krieg, C., Nowicka, M., Guglietta, S., Schindler, S., Hartmann, F. J., Weber, L. M., Dummer, R., Robinson, M. D., Levesque, M. P., & Becher, B. (2018) · Nature Medicine · doi:10.1038/nm.4466 · Cited by 735 · see citing works
Comparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634 · see citing works
Removing technical variability in RNA-seq data using conditional quantile normalization
Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607 · see citing works
Multi-omic machine learning predictor of breast cancer therapy response
Sammut, S.-J., Crispin-Ortuzar, M., Chin, S.-F., Provenzano, E., Bardwell, H. A., Ma, W., Cope, W., Dariush, A., Dawson, S.-J., Abraham, J. E., et al. (2022) · Nature · doi:10.1038/s41586-021-04278-5 · Cited by 654 · see citing works
Giacomelli, E., Meraviglia, V., Campostrini, G., Cochrane, A., Cao, X., van Helden, R. W. J., Krotenberg Garcia, A., Mircea, M., Kostidis, S., Davis, R. P., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.05.004 · Cited by 622 · see citing works
Tumour hypoxia causes DNA hypermethylation by reducing TET activity
Thienpont, B., Steinbacher, J., Zhao, H., D’Anna, F., Kuchnio, A., Ploumakis, A., Ghesquière, B., Van Dyck, L., Boeckx, B., Schoonjans, L., et al. (2016) · Nature · doi:10.1038/nature19081 · Cited by 635 · see citing works
Self-organization and symmetry breaking in intestinal organoid development
Serra, D., Mayr, U., Boni, A., Lukonin, I., Rempfler, M., Challet Meylan, L., Stadler, M. B., Strnad, P., Papasaikas, P., Vischi, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1146-y · Cited by 561 · see citing works
Rodig, S. J., Gusenleitner, D., Jackson, D. G., Gjini, E., Giobbie-Hurder, A., Jin, C., Chang, H., Lovitch, S. B., Horak, C., Weber, J. S., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aar3342 · Cited by 579 · see citing works
Neurotoxic reactive astrocytes induce cell death via saturated lipids
Guttenplan, K. A., Weigel, M. K., Prakash, P., Wijewardhane, P. R., Hasel, P., Rufen-Blanchette, U., Münch, A. E., Blum, J. A., Fine, J., Neal, M. C., et al. (2021) · Nature · doi:10.1038/s41586-021-03960-y · Cited by 578 · see citing works
Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy
Rehman, S. K., Haynes, J., Collignon, E., Brown, K. R., Wang, Y., Nixon, A. M. L., Bruce, J. P., Wintersinger, J. A., Singh Mer, A., Lo, E. B. L., et al. (2021) · Cell · doi:10.1016/j.cell.2020.11.018 · Cited by 495 · see citing works
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497 · see citing works
A single-cell atlas of the human healthy airways
Deprez, M., Zaragosi, L.-E., Truchi, M., Garcia, S. R., Arguel, M.-J., Lebrigand, K., Paquet, A., Pee’r, D., Marquette, C.-H., Leroy, S., et al. (2019) · doi:10.1101/2019.12.21.884759 · Cited by 493 · see citing works
Mathys, H., Peng, Z., Boix, C. A., Victor, M. B., Leary, N., Babu, S., Abdelhady, G., Jiang, X., Ng, A. P., Ghafari, K., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.039 · Cited by 495 · see citing works
Distinct descending motor cortex pathways and their roles in movement
Economo, M. N., Viswanathan, S., Tasic, B., Bas, E., Winnubst, J., Menon, V., Graybuck, L. T., Nguyen, T. N., Smith, K. A., Yao, Z., et al. (2018) · Nature · doi:10.1038/s41586-018-0642-9 · Cited by 415 · see citing works
Cohesin is positioned in mammalian genomes by transcription, CTCF and Wapl
Busslinger, G. A., Stocsits, R. R., van der Lelij, P., Axelsson, E., Tedeschi, A., Galjart, N., & Peters, J.-M. (2017) · Nature · doi:10.1038/nature22063 · Cited by 468 · see citing works
Epigenetic regulation of brain region-specific microglia clearance activity
Ayata, P., Badimon, A., Strasburger, H. J., Duff, M. K., Montgomery, S. E., Loh, Y.-H. E., Ebert, A., Pimenova, A. A., Ramirez, B. R., Chan, A. T., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0192-3 · Cited by 460 · see citing works
Robustly detecting differential expression in RNA sequencing data using observation weights
Zhou, X., Lindsay, H., & Robinson, M. D. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku310 · Cited by 387 · see citing works
High-throughput automated organoid culture via stem-cell aggregation in microcavity arrays
Brandenberg, N., Hoehnel, S., Kuttler, F., Homicsko, K., Ceroni, C., Ringel, T., Gjorevski, N., Schwank, G., Coukos, G., Turcatti, G., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0565-2 · Cited by 390 · see citing works
Daemen, S., Gainullina, A., Kalugotla, G., He, L., Chan, M. M., Beals, J. W., Liss, K. H., Klein, S., Feldstein, A. E., Finck, B. N., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2020.108626 · Cited by 378 · see citing works
“All gene counts were then imported into the R/Bioconductor package EdgeR and TMM normalization size factors were calculated to adjust for differences in library size across all samples (McCarthy et al., 2012; Robinson et al., 2010).”
Hypoxia induces rapid changes to histone methylation and reprograms chromatin
Batie, M., Frost, J., Frost, M., Wilson, J. W., Schofield, P., & Rocha, S. (2019) · Science · doi:10.1126/science.aau5870 · Cited by 385 · see citing works
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
Zhu, C., Yu, M., Huang, H., Juric, I., Abnousi, A., Hu, R., Lucero, J., Behrens, M. M., Hu, M., & Ren, B. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344 · see citing works
Tushev, G., Glock, C., Heumüller, M., Biever, A., Jovanovic, M., & Schuman, E. M. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.030 · Cited by 377 · see citing works
Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference
Huang, Y., McCarthy, D. J., & Stegle, O. (2019) · Genome Biology · doi:10.1186/s13059-019-1865-2 · Cited by 348 · see citing works
Self-citationYilmaz, M., Yalcin, E., Presumey, J., Aw, E., Ma, M., Whelan, C. W., Stevens, B., McCarroll, S. A., & Carroll, M. C. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00763-8 · Cited by 321 · see citing works
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328 · see citing works
Self-citationPower analysis and sample size estimation for RNA-Seq differential expression
Ching, T., Huang, S., & Garmire, L. X. (2014) · RNA · doi:10.1261/rna.046011.114 · Cited by 263 · see citing works
Frodermann, V., Rohde, D., Courties, G., Severe, N., Schloss, M. J., Amatullah, H., McAlpine, C. S., Cremer, S., Hoyer, F. F., Ji, F., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0633-x · Cited by 292 · see citing works
A selective gut bacterial bile salt hydrolase alters host metabolism
Yao, L., Seaton, S. C., Ndousse-Fetter, S., Adhikari, A. A., DiBenedetto, N., Mina, A. I., Banks, A. S., Bry, L., & Devlin, A. S. (2018) · eLife · doi:10.7554/elife.37182 · Cited by 274 · see citing works
Transcriptional and epigenomic landscapes of CNS and non-CNS vascular endothelial cells
Sabbagh, M. F., Heng, J. S., Luo, C., Castanon, R. G., Nery, J. R., Rattner, A., Goff, L. A., Ecker, J. R., & Nathans, J. (2018) · eLife · doi:10.7554/elife.36187 · Cited by 245 · see citing works
Stem-like intestinal Th17 cells give rise to pathogenic effector T cells during autoimmunity
Schnell, A., Huang, L., Singer, M., Singaraju, A., Barilla, R. M., Regan, B. M. L., Bollhagen, A., Thakore, P. I., Dionne, D., Delorey, T. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.018 · Cited by 268 · see citing works
Sacco, K., Castagnoli, R., Vakkilainen, S., Liu, C., Delmonte, O. M., Oguz, C., Kaplan, I. M., Alehashemi, S., Burbelo, P. D., Bhuyan, F., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01724-3 · Cited by 247 · see citing works
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies
Reichart, D., Lindberg, E. L., Maatz, H., Miranda, A. M. A., Viveiros, A., Shvetsov, N., Gärtner, A., Nadelmann, E. R., Lee, M., Kanemaru, K., et al. (2022) · Science · doi:10.1126/science.abo1984 · Cited by 240 · see citing works
Hwang, S., Kwon, A.-Y., Jeong, J.-Y., Kim, S., Kang, H., Park, J., Kim, J.-H., Han, O. J., Lim, S. M., & An, H. J. (2020) · Scientific Reports · doi:10.1038/s41598-019-57218-9 · Cited by 215 · see citing works
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228 · see citing works
Testing for differential abundance in mass cytometry data
Lun, A. T. L., Richard, A. C., & Marioni, J. C. (2017) · Nature Methods · doi:10.1038/nmeth.4295 · Cited by 181 · see citing works
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · see citing works
Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data
Lun, A. T. L., & Marioni, J. C. (2017) · Biostatistics · doi:10.1093/biostatistics/kxw055 · Cited by 159 · see citing works
Kang, K., Park, S. H., Chen, J., Qiao, Y., Giannopoulou, E., Berg, K., Hanidu, A., Li, J., Nabozny, G., Kang, K., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.07.017 · Cited by 196 · see citing works
Hayashi, M., Hinckley, C. A., Driscoll, S. P., Moore, N. J., Levine, A. J., Hilde, K. L., Sharma, K., & Pfaff, S. L. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.023 · Cited by 188 · see citing works
The Cancer SENESCopedia: A delineation of cancer cell senescence
Jochems, F., Thijssen, B., De Conti, G., Jansen, R., Pogacar, Z., Groot, K., Wang, L., Schepers, A., Wang, C., Jin, H., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109441 · Cited by 201 · see citing works
Nutrient sensing modulates malaria parasite virulence
Mancio-Silva, L., Slavic, K., Grilo Ruivo, M. T., Grosso, A. R., Modrzynska, K. K., Vera, I. M., Sales-Dias, J., Gomes, A. R., MacPherson, C. R., Crozet, P., et al. (2017) · Nature · doi:10.1038/nature23009 · Cited by 169 · see citing works
He, L., Davila-Velderrain, J., Sumida, T. S., Hafler, D. A., Kellis, M., & Kulminski, A. M. (2021) · Communications Biology · doi:10.1038/s42003-021-02146-6 · Cited by 181 · see citing works
pSILAC mass spectrometry reveals ZFP91 as IMiD-dependent substrate of the CRL4CRBN ubiquitin ligase
An, J., Ponthier, C. M., Sack, R., Seebacher, J., Stadler, M. B., Donovan, K. A., & Fischer, E. S. (2017) · Nature Communications · doi:10.1038/ncomms15398 · Cited by 176 · see citing works
Targeting histone acetylation dynamics and oncogenic transcription by catalytic P300/CBP inhibition
Hogg, S. J., Motorna, O., Cluse, L. A., Johanson, T. M., Coughlan, H. D., Raviram, R., Myers, R. M., Costacurta, M., Todorovski, I., Pijpers, L., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.04.015 · Cited by 170 · see citing works
Differential but complementary HIF1α and HIF2α transcriptional regulation
Downes, N. L., Laham-Karam, N., Kaikkonen, M. U., & Ylä-Herttuala, S. (2018) · Molecular Therapy · doi:10.1016/j.ymthe.2018.05.004 · Cited by 166 · see citing works
Neuro-mesenchymal units control ILC2 and obesity via a brain–adipose circuit
Cardoso, F., Klein Wolterink, R. G. J., Godinho-Silva, C., Domingues, R. G., Ribeiro, H., da Silva, J. A., Mahú, I., Domingos, A. I., & Veiga-Fernandes, H. (2021) · Nature · doi:10.1038/s41586-021-03830-7 · Cited by 159 · see citing works
Myeloid cell heterogeneity in cancer: not a single cell alike
Kiss, M., Van Gassen, S., Movahedi, K., Saeys, Y., & Laoui, D. (2018) · Cellular Immunology · doi:10.1016/j.cellimm.2018.02.008 · Cited by 159 · see citing works
Functional Dynamics of the Gut Microbiome in Elderly People during Probiotic Consumption
Eloe-Fadrosh, E. A., Brady, A., Crabtree, J., Drabek, E. F., Ma, B., Mahurkar, A., Ravel, J., Haverkamp, M., Fiorino, A.-M., Botelho, C., et al. (2015) · mBio · doi:10.1128/mbio.00231-15 · Cited by 160 · see citing works
Beta-Poisson model for single-cell RNA-seq data analyses
Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M., & Pawitan, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw202 · Cited by 157 · see citing works
Notaras, M., Lodhi, A., Dündar, F., Collier, P., Sayles, N. M., Tilgner, H., Greening, D., & Colak, D. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01316-6 · Cited by 150 · see citing works
Interleukin-13 drives metabolic conditioning of muscle to endurance exercise
Knudsen, N. H., Stanya, K. J., Hyde, A. L., Chalom, M. M., Alexander, R. K., Liou, Y.-H., Starost, K. A., Gangl, M. R., Jacobi, D., Liu, S., et al. (2020) · Science · doi:10.1126/science.aat3987 · Cited by 139 · see citing works
Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy
Hartmann, F. J., Babdor, J., Gherardini, P. F., Amir, E.-A. D., Jones, K., Sahaf, B., Marquez, D. M., Krutzik, P., O’Donnell, E., Sigal, N., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.06.049 · Cited by 124 · see citing works
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 · see citing works
Self-citationBarengolts, E., Green, S. J., Eisenberg, Y., Akbar, A., Reddivari, B., Layden, B. T., Dugas, L., & Chlipala, G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194171 · Cited by 111 · see citing works
Zhou, Z., Van der Jeught, K., Fang, Y., Yu, T., Li, Y., Ao, Z., Liu, S., Zhang, L., Yang, Y., Eyvani, H., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00805-x · Cited by 107 · see citing works
Prakash, P., Manchanda, P., Paouri, E., Bisht, K., Sharma, K., Rajpoot, J., Wendt, V., Hossain, A., Wijewardhane, P. R., Randolph, C. E., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.04.029 · Cited by 104 · see citing works
Self-citationRNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis
Williams, A. G., Thomas, S., Wyman, S. K., & Holloway, A. K. (2014) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg1113s83 · Cited by 93 · see citing works
A survey of the mouse hindbrain in the fed and fasted states using single-nucleus RNA sequencing
Dowsett, G. K. C., Lam, B. Y. H., Tadross, J. A., Cimino, I., Rimmington, D., Coll, A. P., Polex-Wolf, J., Knudsen, L. B., Pyke, C., & Yeo, G. S. H. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101240 · Cited by 98 · see citing works
Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility
Meijer, M., Agirre, E., Kabbe, M., van Tuijn, C. A., Heskol, A., Zheng, C., Mendanha Falcão, A., Bartosovic, M., Kirby, L., Calini, D., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.034 · Cited by 98 · see citing works
Rossaert, E., Pollari, E., Jaspers, T., Van Helleputte, L., Jarpe, M., Van Damme, P., De Bock, K., Moisse, M., & Van Den Bosch, L. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0750-2 · Cited by 93 · see citing works
Low-dose rapamycin extends lifespan in a mouse model of mtDNA depletion syndrome
Siegmund, S. E., Yang, H., Sharma, R., Javors, M., Skinner, O., Mootha, V., Hirano, M., & Schon, E. A. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx341 · Cited by 92 · see citing works
Immune correlates of tuberculosis disease and risk translate across species
Ahmed, M., Thirunavukkarasu, S., Rosa, B. A., Thomas, K. A., Das, S., Rangel-Moreno, J., Lu, L., Mehra, S., Mbandi, S. K., Thackray, L. B., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay0233 · Cited by 89 · see citing works
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 · see citing works
Hung, Y.-F., Chen, C.-Y., Shih, Y.-C., Liu, H.-Y., Huang, C.-M., & Hsueh, Y.-P. (2018) · Journal of Cell Biology · doi:10.1083/jcb.201712113 · Cited by 86 · see citing works
Cai, N., Gomez-Duran, A., Yonova-Doing, E., Kundu, K., Burgess, A. I., Golder, Z. J., Calabrese, C., Bonder, M. J., Camacho, M., Lawson, R. A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01441-3 · Cited by 73 · see citing works
Discovery and characterization of variance QTLs in human induced pluripotent stem cells
Sarkar, A. K., Tung, P.-Y., Blischak, J. D., Burnett, J. E., Li, Y. I., Stephens, M., & Gilad, Y. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008045 · Cited by 72 · see citing works
Transcription-dependent cohesin repositioning rewires chromatin loops in cellular senescence
Olan, I., Parry, A. J., Schoenfelder, S., Narita, M., Ito, Y., Chan, A. S. L., Slater, G. S. C., Bihary, D., Bando, M., Shirahige, K., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19878-4 · Cited by 76 · see citing works
Araujo, D. J., Toriumi, K., Escamilla, C. O., Kulkarni, A., Anderson, A. G., Harper, M., Usui, N., Ellegood, J., Lerch, J. P., Birnbaum, S. G., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.1005-17.2017 · Cited by 73 · see citing works
Delbridge, A. R. D., Huh, D., Brickelmaier, M., Burns, J. C., Roberts, C., Challa, R., Raymond, N., Cullen, P., Carlile, T. M., Ennis, K. A., et al. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.592005 · Cited by 72 · see citing works
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 · see citing works
Self-citationYang, H. S., Onos, K. D., Choi, K., Keezer, K. J., Skelly, D. A., Carter, G. W., & Howell, G. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108739 · Cited by 69 · see citing works
Deconstructing Stepwise Fate Conversion of Human Fibroblasts to Neurons by MicroRNAs
Cates, K., McCoy, M. J., Kwon, J.-S., Liu, Y., Abernathy, D. G., Zhang, B., Liu, S., Gontarz, P., Kim, W. K., Chen, S., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2020.08.015 · Cited by 67 · see citing works
Schenk, S., Bannister, S. C., Sedlazeck, F. J., Anrather, D., Minh, B. Q., Bileck, A., Hartl, M., von Haeseler, A., Gerner, C., Raible, F., et al. (2019) · eLife · doi:10.7554/elife.41556 · Cited by 59 · see citing works
ASGARD is a single-cell guided pipeline to aid repurposing of drugs
He, B., Xiao, Y., Liang, H., Huang, Q., Du, Y., Li, Y., Garmire, D., Sun, D., & Garmire, L. X. (2023) · Nature Communications · doi:10.1038/s41467-023-36637-3 · Cited by 57 · see citing works
Chandra, S., Di Meco, A., Dodiya, H. B., Popovic, J., Cuddy, L. K., Weigle, I. Q., Zhang, X., Sadleir, K., Sisodia, S. S., & Vassar, R. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00635-2 · Cited by 58 · see citing works
Molecular Logic of Spinocerebellar Tract Neuron Diversity and Connectivity
Baek, M., Menon, V., Jessell, T. M., Hantman, A. W., & Dasen, J. S. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.04.113 · Cited by 56 · see citing works
Heger, L., Hatscher, L., Liang, C., Lehmann, C. H. K., Amon, L., Lühr, J. J., Kaszubowski, T., Nzirorera, R., Schaft, N., Dörrie, J., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300343120 · Cited by 54 · see citing works
Thakore, P. I., Schnell, A., Zhao, M., Huang, L., Hou, Y., Christian, E., Zaghouani, S., Wang, C., Singh, V., Ma, S., et al. (2022) · doi:10.1101/2022.02.26.482041 · Cited by 57 · see citing works
- Authors
- McCarthy, D. J., Chen, Y., & Smyth, G. K.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Smyth, Gordon K.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01-28
- Volume
- 40
- Issue
- 10
- Pages
- 4288-4297
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3378882
- OpenAlex
- W2130116522
- Cited by
- 5,195
- References
- 70
- Citations by year
- 2026: 203 · 2025: 339 · 2024: 496 · 2023: 527 · 2022: 680 · 2021: 828 · 2020: 640 · 2019: 463 · 2018: 354 · 2017: 237 · 2016: 157 · 2015: 110 · 2014: 92 · 2013: 47 · 2012: 18
- FWCI
- 34.30
- 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
- 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 › Single-cell and spatial transcriptomics
- Keywords
- differential gene expression, RNA-seq read counts, biological variation, technical variability, empirical Bayes, edgeR, generalized linear models, paired design, carcinoma, profile likelihood, count data analysis, DNA sequencing, ChIP-seq, DNA methylation
- MeSH terms
- Algorithms, Bayes Theorem, Carcinoma, Squamous Cell, Mouth Neoplasms, Genetic Variation, Linear Models, Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
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
