ROAST: rotation gene set tests for complex microarray experiments
Open accessAbstract
MOTIVATION: A gene set test is a differential expression analysis in which a P-value is assigned to a set of genes as a unit. Gene set tests are valuable for increasing statistical power, organizing and interpreting results and for relating expression patterns across different experiments. Existing methods are based on permutation. Methods that rely on permutation of probes unrealistically assume independence of genes, while those that rely on permutation of sample are suitable only for two-group comparisons with a good number of replicates in each group. RESULTS: We present ROAST, a statistically rigorous gene set test that allows for gene-wise correlation while being applicable to almost any experimental design. Instead of permutation, ROAST uses rotation, a Monte Carlo technology for multivariate regression. Since the number of rotations does not depend on sample size, ROAST gives useful results even for experiments with minimal replication. ROAST allows for any experimental design that can be expressed as a linear model, and can also incorporate array weights and correlated samples. ROAST can be tuned for situations in which only a subset of the genes in the set are actively involved in the molecular pathway. ROAST can test for uni- or bi-direction regulation. Probes can also be weighted to allow for prior importance. The power and size of the ROAST procedure is demonstrated in a simulation study, and compared to that of a representative permutation method. Finally, ROAST is used to test the degree of transcriptional conservation between human and mouse mammary stems. AVAILABILITY: ROAST is implemented as a function in the Bioconductor package limma available from www.bioconductor.org.
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?
Rotation technology permits statistical significance to be tested for sets of genes for any linear model contrast.
“One way that this is done is by rotation technology, which permits statistical significance to be tested for sets of genes for any linear model contrast (55).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [55]
The roast and mroast functions implement a self-contained test of whether any worthwhile subset of the specified set of genes is differentially expressed.
“The roast and mroast functions implement a self-contained test of whether any worthwhile subset of the specified set of genes is differentially expressed (55).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [55]
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
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
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829 · see citing works
Self-citationMootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197 · see citing works
kConFab, Lim, E., Vaillant, F., Wu, D., Forrest, N. C., Pal, B., Hart, A. H., Asselin-Labat, M.-L., Gyorki, D. E., Ward, T., et al. (2009) · Nature Medicine · doi:10.1038/nm.2000 · Cited by 1,492 · see citing works
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 · see citing works
Self-citationA global test for groups of genes: testing association with a clinical outcome
Goeman, J. J., van de Geer, S., de Kort, F., & van Houwelingen, H. C. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg382 · Cited by 1,052 · see citing works
Permutation Methods: A Basis for Exact Inference
Ernst, M. (2004) · Statistical Science · doi:10.1214/088342304000000396 · Cited by 858 · see citing works
Analyzing gene expression data in terms of gene sets: methodological issues
Goeman, J. J., & Bühlmann, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm051 · Cited by 795 · see citing works
On testing the significance of sets of genes
Efron, B., & Tibshirani, R. J. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas101 · Cited by 785 · see citing works
Lönnstedt, I. M. (2001) · Cited by 670 · see citing works
Wright, G. W. J., & Simon, R. M. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg345 · Cited by 662 · see citing works
Discovering statistically significant pathways in expression profiling studies
Tian, L., Greenberg, S. A., Kong, S. W., Altschuler, J., Kohane, I. S., & Park, P. J. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506577102 · Cited by 649 · see citing works
Diboun, I., Wernisch, L., Orengo, C. A., & Koltzenburg, M. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-252 · Cited by 593 · see citing works
Lim, E., Wu, D., Pal, B., Bouras, T., Asselin-Labat, M.-L., Vaillant, F., Yagita, H., Lindeman, G. J., Smyth, G. K., & Visvader, J. E. (2010) · Breast Cancer Research · doi:10.1186/bcr2560 · Cited by 440 · see citing works
Self-citationA general modular framework for gene set enrichment analysis
Ackermann, M., & Strimmer, K. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-47 · Cited by 358 · 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-citationImproving gene set analysis of microarray data by SAM-GS
Dinu, I. A., Potter, J. D., Mueller, T., Liu, Q., Adewale, A. J., Jhangri, G. S., Einecke, G., Famulski, K. S., Halloran, P. F., & Yasui, Y. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-242 · Cited by 262 · see citing works
Extensions to gene set enrichment
Jiang, Z., & Gentleman, R. C. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl599 · Cited by 216 · see citing works
Group testing for pathway analysis improves comparability of different microarray datasets
Manoli, T., Gretz, N., Gröne, H., Kenzelmann, M., Eils, R., & Brors, B. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl424 · Cited by 150 · see citing works
GlobalANCOVA: exploration and assessment of gene group effects
Hummel, M., Meister, R., & Mansmann, U. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm531 · Cited by 148 · see citing works
Langsrud, Ø. (2005) · Statistics and Computing · doi:10.1007/s11222-005-4789-5 · Cited by 121 · see citing works
New Nonparametric Tests of Multivariate Locations and Scales Using Data Depth
Li, J., & Liu, R. Y. (2004) · Statistical Science · doi:10.1214/088342304000000594 · Cited by 92 · see citing works
Mammary Stem Cells and Mammopoiesis
Visvader, J. E., & Lindeman, G. J. (2006) · Cancer Research · doi:10.1158/0008-5472.can-06-2254 · Cited by 87 · see citing works
Self-citationComparison of small n statistical tests of differential expression applied to microarrays
Murie, C., Woody, O. Z., Lee, A. Y., & Nadon, R. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-45 · Cited by 78 · see citing works
Significance testing for small microarray experiments
Kooperberg, C. L., Aragaki, A. K., Strand, A. D., & Olson, J. M. (2005) · Statistics in Medicine · doi:10.1002/sim.2109 · Cited by 64 · see citing works
An Integrated Approach for the Analysis of Biological Pathways using Mixed Models
Wang, L., Zhang, B., Wolfinger, R. D., & Chen, X. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000115 · Cited by 62 · see citing works
Gene set enrichment analysis using linear models and diagnostics
Oron, A. P., Jiang, Z., & Gentleman, R. C. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn465 · Cited by 61 · see citing works
Absolute enrichment: gene set enrichment analysis for homeostatic systems
Saxena, V., Orgill, D. P., & Kohane, I. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl766 · Cited by 58 · see citing works
Pathway Analysis of Microarray Data via Regression
Adewale, A. J., Dinu, I. A., Potter, J. D., et al. (2008) · Journal of Computational Biology · doi:10.1089/cmb.2008.0002 · Cited by 29 · see citing works
Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments
Dørum, G., Snipen, L. G., Solheim, M., & Sæbø, S. (2009) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1418 · Cited by 26 · see citing works
Lim, E., Wu, D., Pal, B., et al. (2010) · Cancer Research · doi:10.1158/0008-5472.sabcs10-p4-04-03 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Self-citationGSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440 · 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-citationHobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes
Mackay, L. K., Minnich, M., Kragten, N. A. M., Liao, Y., Nota, B., Seillet, C., Zaid, A., Man, K., Preston, S., Freestone, D., et al. (2016) · Science · doi:10.1126/science.aad2035 · Cited by 1,010 · 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
Cantelmo, A. R., Conradi, L.-C., Brajic, A., Goveia, J., Kalucka, J., Pircher, A., Chaturvedi, P., Hol, J., Thienpont, B., Teuwen, L.-A., et al. (2016) · Cancer Cell · doi:10.1016/j.ccell.2016.10.006 · Cited by 659 · see citing works
Cretney, E., Xin, A., Shi, W., Minnich, M., Masson, F., Miasari, M., Belz, G. T., Smyth, G. K., Busslinger, M., Nutt, S. L., et al. (2011) · Nature Immunology · doi:10.1038/ni.2006 · Cited by 605 · see citing works
Self-citationRapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition
Xue, J. Y., Zhao, Y., Aronowitz, J., Mai, T. T., Vides, A., Qeriqi, B., Kim, D., Li, C., de Stanchina, E., Mazutis, L., et al. (2020) · Nature · doi:10.1038/s41586-019-1884-x · Cited by 543 · see citing works
BET inhibitor resistance emerges from leukaemia stem cells
Fong, C. Y., Gilan, O., Lam, E. Y. N., Rubin, A. F., Ftouni, S., Tyler, D., Stanley, K., Sinha, D., Yeh, P., Morison, J., et al. (2015) · Nature · doi:10.1038/nature14888 · Cited by 545 · see citing works
Acceleration of β Cell Aging Determines Diabetes and Senolysis Improves Disease Outcomes
Aguayo-Mazzucato, C., Andle, J., Lee, T. B., Midha, A., Talemal, L., Chipashvili, V., Hollister-Lock, J., van Deursen, J., Weir, G., & Bonner-Weir, S. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.05.006 · Cited by 469 · see citing works
Hoefflin, R., Harlander, S., Schäfer, S., Metzger, P., Kuo, F., Schönenberger, D., Adlesic, M., Peighambari, A., Seidel, P., Chen, C.-Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17873-3 · Cited by 292 · see citing works
Metastatic recurrence in colorectal cancer arises from residual EMP1+ cells
Cañellas-Socias, A., Cortina, C., Hernando-Momblona, X., Palomo-Ponce, S., Mulholland, E. J., Turon, G., Mateo, L., Conti, S., Roman, O., Sevillano, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05402-9 · Cited by 256 · see citing works
Yi, M., Zhang, J., Li, A., Niu, M., Yan, Y., Jiao, Y., Luo, S., Zhou, P., & Wu, K. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01045-x · Cited by 233 · see citing works
Probiotic Bacillus subtilis protects against α-synuclein aggregation in C. elegans
Goya, M. E., Xue, F., Sampedro-Torres-Quevedo, C., Arnaouteli, S., Riquelme-Dominguez, L., Romanowski, A., Brydon, J., Ball, K. L., Stanley-Wall, N. R., & Doitsidou, M. (2020) · Cell Reports · doi:10.1016/j.celrep.2019.12.078 · Cited by 205 · see citing works
ChIP-Enrich: gene set enrichment testing for ChIP-seq data
Welch, R. P., Lee, C., Imbriano, P. M., Patil, S., Weymouth, T. E., Smith, R. A., Scott, L. J., & Sartor, M. A. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku463 · Cited by 151 · see citing works
Yi, M., Niu, M., Zhang, J., Li, S., Zhu, S., Yan, Y., Li, N., Zhou, P., Chu, Q., & Wu, K. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01155-6 · Cited by 163 · see citing works
Development of an in vitro human liver system for interrogating nonalcoholic steatohepatitis
Feaver, R. E., Cole, B. K., Lawson, M. J., Hoang, S. A., Marukian, S., Blackman, B. R., Figler, R. A., Sanyal, A. J., Wamhoff, B. R., & Dash, A. (2016) · JCI Insight · doi:10.1172/jci.insight.90954 · Cited by 153 · see citing works
Multi‐omic rejuvenation of naturally aged tissues by a single cycle of transient reprogramming
Chondronasiou, D., Gill, D., Mosteiro, L., Urdinguio, R. G., Berenguer‐Llergo, A., Aguilera, M., Durand, S., Aprahamian, F., Nirmalathasan, N., Abad, M., et al. (2022) · Aging Cell · doi:10.1111/acel.13578 · Cited by 133 · see citing works
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 · see citing works
The efficacy of chemotherapy is limited by intratumoral senescent cells expressing PD-L2
Chaib, S., López-Domínguez, J. A., Lalinde-Gutiérrez, M., Prats, N., Marin, I., Boix, O., García-Garijo, A., Meyer, K., Muñoz, M. I., Aguilera, M., et al. (2024) · Nature Cancer · doi:10.1038/s43018-023-00712-x · Cited by 114 · see citing works
Ginno, P. A., Burger, L., Seebacher, J., Iesmantavicius, V., & Schübeler, D. (2018) · Nature Communications · doi:10.1038/s41467-018-06007-5 · Cited by 91 · see citing works
Dedifferentiation and neuronal repression define familial Alzheimer’s disease
Caldwell, A. B., Liu, Q., Schroth, G. P., Galasko, D. R., Yuan, S. H., Wagner, S. L., & Subramaniam, S. (2020) · Science Advances · doi:10.1126/sciadv.aba5933 · Cited by 69 · see citing works
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 · see citing works
TWIST1 Homodimers and Heterodimers Orchestrate Lineage-Specific Differentiation
Fan, X., Waardenberg, A. J., Demuth, M., Osteil, P., Sun, J. Q. J., Loebel, D. A. F., Graham, M., Tam, P. P. L., & Fossat, N. (2020) · Molecular and Cellular Biology · doi:10.1128/mcb.00663-19 · Cited by 33 · see citing works
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 · see citing works
Self-citationTranscriptomic Signatures of Single-Suture Craniosynostosis Phenotypes
Lapehn, S., Gustafson, J. A., Timms, A. E., Cunningham, M. L., & Paquette, A. G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065353 · Cited by 3 · see citing works
- Authors
- Wu, D., Lim, E., Vaillant, F., Asselin-Labat, M.-L., Visvader, J. E., & Smyth, G. K.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Smyth, Gordon K.
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-07-07
- Volume
- 26
- Issue
- 17
- Pages
- 2176-2182
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2922896
- OpenAlex
- W2095900642
- Cited by
- 560
- References
- 31
- Citations by year
- 2026: 25 · 2025: 29 · 2024: 52 · 2023: 45 · 2022: 39 · 2021: 68 · 2020: 57 · 2019: 40 · 2018: 30 · 2017: 42 · 2016: 30 · 2015: 42 · 2014: 19 · 2013: 16 · 2012: 20
- FWCI
- 5.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
- Funders
- Medical Research Council
- National Health and Medical Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- 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
- Keywords
- differential gene expression, microarray data, rotation, pathway analysis, mammary stem cells, human-mouse comparison, limma
- MeSH terms
- Algorithms, Animals, Humans, Linear Models, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice
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
