Consensus framework for exploring microarray data using multiple clustering methods.

Consensus framework for exploring microarray data using multiple clustering methods. Research Abstract Details 

Research Abstract Table of Contents

Jump to the:

Consensus framework for exploring microarray data using multiple clustering methods. Abstract Text:

Ted Laderas,Shannon McWeeney,

The large variety of clustering algorithms and their variants can be daunting to researchers wishing to explore patterns within their microarray datasets. Furthermore, each clustering method has distinct biases in finding patterns within the data, and clusterings may not be reproducible across different algorithms. A consensus approach utilizing multiple algorithms can show where the various methods agree and expose robust patterns within the data. In this paper, we present a software package - Consense, written for R/Bioconductor - that utilizes such an approach to explore microarray datasets. Consense produces clustering results for each of the clustering methods and produces a report of metrics comparing the individual clusterings. A feature of Consense is identification of genes that cluster consistently with an index gene across methods. Utilizing simulated microarray data, sensitivity of the metrics to the biases of the different clustering algorithms is explored. The framework is easily extensible, allowing this tool to be used by other functional genomic data types, as well as other high-throughput OMICS data types generated from metabolomic and proteomic experiments. It also provides a flexible environment to benchmark new clustering algorithms. Consense is currently available as an installable R/Bioconductor package (http://www.ohsucancer.com/isrdev/consense/).

Consensus framework for exploring microarray data using multiple clustering methods. Publishing Authors By Initials

T Laderas,S McWeeney,

For similar information science: computing methodologies: software research abstracts see: information science: computing methodologies: software research

PUBMED ID PMID:

MEDLINE DATE:

Consensus framework for exploring microarray data using multiple clustering methods. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Omics : a journal of integrative biology

VOLUME: 11

Page Numbers: 116-28

Journal Abbreviation:

ISSN: 1536-2310

DAY: 3

MONTH: 12

YEAR: 2007

Consensus framework for exploring microarray data using multiple clustering methods. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 101131135

Consensus framework for exploring microarray data using multiple clustering methods. Keywords Mesh Terms:

KEYWORDS: Software

MESH TERMS: methods

Chemical & Substance for Abstract: Consensus framework for exploring microarray data using multiple clustering methods. Information

Substance Name: Fungal Proteins

Registry Number: 0

Grant and Affiliation Information for Consensus framework for exploring microarray data using multiple clustering methods.

AFFILIATION: Informatics Shared Resource, OHSU Cancer Institute, Portland, Oregon 97201, USA. laderast@ohsu.edu

Country: United States

AGENCY: United States NHLBI

GRANT: R01 HL072321

ACRONYM: HL

MEDLINETA: OMICS

REFSOURCE:

DATABASENAME:

ACCESSION NUMBER:

Number Hits: 0

Consensus framework for exploring microarray data using multiple clustering methods Related Publications

• "Doctors' orders".
• Consensus framework for exploring microarray data using multiple clustering methods.
• Localization of TFIIB binding regions using serial analysis of chromatin occupancy.
• Mapping cellular transcriptosomes in autopsied Alzheimer's disease subjects and relevant animal models.
• TandTRAQ: an open-source tool for integrated protein identification and quantitation.