How does gene expression clustering work?
- 1 December 2005
- journal article
- review article
- Published by Springer Science and Business Media LLC in Nature Biotechnology
- Vol. 23 (12), 1499-1501
- https://doi.org/10.1038/nbt1205-1499
Abstract
Clustering is often one of the first steps in gene expression analysis. How do clustering algorithms work, which ones should we use and what can we expect from them? Our ability to gather genome-wide expression data has far outstripped the ability of our puny human brains to process the raw data. We can distill the data down to a more comprehensible level by subdividing the genes into a smaller number of categories and then analyzing those.Keywords
This publication has 10 references indexed in Scilit:
- Computational cluster validation in post-genomic data analysisBioinformatics, 2005
- Cluster analysis for gene expression data: a surveyIEEE Transactions on Knowledge and Data Engineering, 2004
- Comparative analysis of clustering methods for gene expression time course dataGenetics and Molecular Biology, 2004
- Scoring clustering solutions by their biological relevanceBioinformatics, 2003
- Comparisons and validation of statistical clustering techniques for microarray gene expression dataBioinformatics, 2003
- Judging the Quality of Gene Expression-Based Clustering Methods Using Gene AnnotationGenome Research, 2002
- Systematic determination of genetic network architectureNature Genetics, 1999
- Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiationProceedings of the National Academy of Sciences of the United States of America, 1999
- Cluster analysis and display of genome-wide expression patternsProceedings of the National Academy of Sciences of the United States of America, 1998
- Cluster AnalysisPublished by SAGE Publications ,1984