The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A global test for groups of genes: testing association with a clinical outcome
Open accessAbstract
Motivation: This paper presents a global test to be used for the analysis of microarray data. Using this test it can be determined whether the global expression pattern of a group of genes is significantly related to some clinical outcome of interest. Groups of genes may be any size from a single gene to all genes on the chip (e.g. known pathways, specific areas of the genome or clusters from a cluster analysis). Result: The test allows groups of genes of different size to be compared, because the test gives one p-value for the group, not a p-value for each gene. Researchers can use the test to investigate hypotheses based on theory or past research or to mine gene ontology databases for interesting pathways. Multiple testing problems do not occur unless many groups are tested. Special attention is given to visualizations of the test result, focussing on the associations between samples and showing the impact of individual genes on the test result. Availability: An R-package globaltest is available from http://www.bioconductor.org
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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
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440
Cited by
GSVA: 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
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
Xia, J., & Wishart, D. S. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.11 · Cited by 1,544
A systems biology approach for pathway level analysis
Draghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A., Voichita, C., Georgescu, C., & Romero, R. (2007) · Genome Research · doi:10.1101/gr.6202607 · Cited by 1,249
A novel signaling pathway impact analysis
Tarca, A. L., Draghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J.-S., Kim, C. J., Kusanovic, J. P., & Romero, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btn577 · Cited by 1,031
'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
Metadata
- Authors
- Goeman, J. J., van de Geer, S. A., de Kort, F., & van Houwelingen, H. C.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-01-01
- Volume
- 20
- Issue
- 1
- Pages
- 93-99
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2118069309
- Cited by
- 1,069
- References
- 3
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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