Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Genetics. 2005 Aug;170(4):2003-11. Epub 2005 Jun 8.

Adding confidence to gene expression clustering.

Author information

  • 1Department of Statistics, Purdue University, West Lafayette, Indiana 47907, USA.

Abstract

It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions or grouped on the basis of profiles that are assessed temporally or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgment of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and conditional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space and show that both methods can provide an effective assessment of gene expression profiles whose coregulation is statistically different from that expected by random chance alone.

PMID:
15944369
[PubMed - indexed for MEDLINE]
PMCID:
PMC1449753
Free PMC Article

Images from this publication.See all images (7)Free text

F igure  1.—
F igure  2.—
F igure  3.—
F igure  4.—
F igure  5.—
F igure  6.—
F igure  7.—
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk