The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments
Open accessAbstract
Motivation: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. Results: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset. Availability: The method has been implemented in the statistical language R and is freely available from the Bioconductor contributed packages repository and from Contact: aconesa@ivia.es; mj.nueda@ua.es
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,923
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,803
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
Harrell, F. E. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,195
Draper, N. R., & Smith, H. (1998) · Wiley Series in Probability and Statistics · doi:10.1002/9781118625590 · Cited by 5,024
Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,722
Rao, S. J. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s263 · Cited by 1,941
Identifying differentially expressed genes using false discovery rate controlling procedures
Reiner, A., Yekutieli, D., & Benjamini, Y. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btf877 · Cited by 1,765
Analysis of Variance for Gene Expression Microarray Data
Kerr, M. K., Martin, M., & Churchill, G. A. (2000) · Journal of Computational Biology · doi:10.1089/10665270050514954 · Cited by 1,310
Cited by
Gene expression across mammalian organ development
Cardoso-Moreira, M., Halbert, J., Valloton, D., Velten, B., Chen, C., Shao, Y., Liechti, A., Ascenção, K., Rummel, C., Ovchinnikova, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1338-5 · Cited by 838
Regeneration of fat cells from myofibroblasts during wound healing
Plikus, M. V., Guerrero-Juarez, C. F., Ito, M., Li, Y. R., Dedhia, P. H., Zheng, Y., Shao, M., Gay, D. L., Ramos, R., Hsi, T.-C., et al. (2017) · Science · doi:10.1126/science.aai8792 · Cited by 584
Muscle insulin sensitivity and glucose metabolism are controlled by the intrinsic muscle clock
Dyar, K. A., Ciciliot, S., Wright, L. E., Biensø, R. S., Tagliazucchi, G. M., Patel, V. R., Forcato, M., Paz, M. I. P., Gudiksen, A., Solagna, F., et al. (2014) · Molecular Metabolism · doi:10.1016/j.molmet.2013.10.005 · Cited by 454
A high-resolution mRNA expression time course of embryonic development in zebrafish
White, R. J., Collins, J. E., Sealy, I. M., Wali, N., Dooley, C. M., Digby, Z., Stemple, D. L., Murphy, D. N., Billis, K., Hourlier, T., et al. (2017) · eLife · doi:10.7554/elife.30860 · Cited by 361
Microglia Actively remodel adult hippocampal neurogenesis through the phagocytosis secretome
Diaz-Aparicio, I., Paris, I., Sierra-Torre, V., Plaza-Zabala, A., Rodríguez-Iglesias, N., Márquez-Ropero, M., Beccari, S., Huguet, P., Abiega, O., Alberdi, E., et al. (2020) · The Journal of Neuroscience · doi:10.1523/jneurosci.0993-19.2019 · Cited by 333
Metadata
- Authors
- Conesa, A., Nueda, M. J., Ferrer, A., & Talón, M.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-02-15
- Volume
- 22
- Issue
- 9
- Pages
- 1096-1102
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2105559737
- Cited by
- 470
- References
- 28
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. https://app.cochat.ai/papers/10.1093/bioinformatics/btl056
- The paper
- Conesa, A., Nueda, M. J., Ferrer, A., & Talón, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics. https://doi.org/10.1093/bioinformatics/btl056
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
