Exploring expression data: identification and analysis of coexpressed genes.

Abstract:

: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.

journal_name

Genome Res

journal_title

Genome research

authors

Heyer LJ,Kruglyak S,Yooseph S

doi

10.1101/gr.9.11.1106

subject

Has Abstract

pub_date

1999-11-01 00:00:00

pages

1106-15

issue

11

eissn

1088-9051

issn

1549-5469

journal_volume

9

pub_type

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