Exploring Expression Data: Identification and Analysis of Coexpressed Genes | Zendy

Laurie J. Heyer | Zendy; Semyon Kruglyak | Zendy; Shibu Yooseph | Zendy

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Exploring Expression Data: Identification and Analysis of Coexpressed Genes

Author(s) -

Laurie J. Heyer,

Semyon Kruglyak,

Shibu Yooseph

Publication year - 1999

Publication title -

genome research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 9.556

H-Index - 297

eISSN - 1549-5469

pISSN - 1088-9051

DOI - 10.1101/gr.9.11.1106

Subject(s) - cluster analysis , false positive paradox , identification (biology) , biology , computational biology , set (abstract data type) , expression (computer science) , similarity (geometry) , gene expression profiling , gene , data set , genome , data mining , computer science , gene expression , genetics , artificial intelligence , botany , image (mathematics) , programming language

Analysis procedures are needed to extract useful information from the large amount of gene expression data that is becoming available. This work describes a set of analytical tools and their application to yeast cell cycle data. The components of our approach are (1) a similarity measure that reduces the number of false positives, (2) a new clustering algorithm designed specifically for grouping gene expression patterns, and (3) an interactive graphical cluster analysis tool that allows user feedback and validation. We use the clusters generated by our algorithm to summarize genome-wide expression and to initiate supervised clustering of genes into biologically meaningful groups.

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