Identifying Coexpressed Genes

Qihua Wang

Chapter 7 in Statistical Methods for Biostatistics and Related Fields, 2007, pp 125-145 from Springer

Abstract: Abstract Some gene expression data contain outliers and noise because of experiment error. In clustering, outliers and noise can result in false positives and false negatives. This motivates us to develop a weighting method to adjust the expression data such that the outlier and noise effect decrease, and hence result in a reduction in false positives and false negatives in clustering. In this paper, we describe the weighting adjustment method and apply it to a yeast cell cycle data set. Based on the adjusted yeast cell cycle expression data, the hierarchical clustering method with a correlation coefficient measure performs better than that based on standardized expression data. The clustering method based on the adjusted data can group some functionally related genes together and yields higher quality clusters.

Keywords: Expression Data; Gene Expression Data; Gene Pair; Small Cluster; Cluster Tree (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-32691-5_7

Ordering information: This item can be ordered from
http://www.springer.com/9783540326915

DOI: 10.1007/978-3-540-32691-5_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().