The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Detecting Stable Clusters Using Principal Component Analysis
Abstract
Clustering is one of the most commonly used tools in the analysis of gene expression data (1,2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is often used as a preliminary step in extracting gene networks and inference of gene function (3,4). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3,5,6), including sensitivity to drugs (7), and can also detect artifacts of experimental conditions (8). Clustering and its applications in biology are presented in greater detail in Chapter 13 (see also ref. 9). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well.
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Cites
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
Discarding Variables in a Principal Component Analysis. I: Artificial Data
Jolliffe, I. T. (1972) · Applied Statistics · doi:10.2307/2346488 · Cited by 907
'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P. (2000) · Genome Biology · doi:10.1186/gb-2000-1-2-research0003 · Cited by 547 · cited 1×
“This is also the basis of the “gene shaving” method (24) that builds a set of variables by iteratively discarding var iables that are least correlated with the leading principal components.”
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Cited by
Fortino, V., Wisgrill, L., Werner, P., Suomela, S., Linder, N., Jalonen, E., Suomalainen, A., Marwah, V., Kero, M., Pesonen, M., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2009192117 · Cited by 99
From observation to information: data-driven understanding of on farm yield variation
Jiménez, D., Dorado, H., Cock, J., Prager, S. D., Delerce, S., Grillon, A., Andrade Bejarano, M., Benavides, H., & Jarvis, A. (2016) · PLOS ONE · doi:10.1371/journal.pone.0150015 · Cited by 47
Huang, H., Huang, X., Zhou, R., Zhou, H., Lee, J. J., & Cen, X. (2024) · Accident Analysis & Prevention · doi:10.1016/j.aap.2024.107616 · Cited by 25
Metadata
- Authors
- Ben-Hur, A., & Guyon, I.
- Venue
- Functional Genomics
- Publisher
- Humana Press
- Published
- 2003-11-14
- Volume
- 224
- Pages
- 159-182
- OpenAlex
- W2416999725
- Cited by
- 252
- References
- 4
- FWCI
- 6.17
- Type
- book chapter
- Language
- en
- Open access
- closed
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