Integration of GO annotations in Correspondence Analysis: facilitating the interpretation of microarray data.
BACKGROUND
The functional interpretation of microarray datasets still represents a time-consuming and challenging task. Up to now functional categories that are relevant for one or more experimental context(s) have been commonly extracted from a set of regulated genes and presented in long lists.


RESULTS
To facilitate interpretation, we integrated Gene Ontology (GO) annotations into Correspondence Analysis to display genes, experimental conditions and gene-annotations in a single plot. The position of the annotations in these plots can be directly used for the functional interpretation of clusters of genes or experimental conditions without the need for comparing long lists of annotations. Correspondence Analysis is not limited in the number of experimental conditions that can be compared simultaneously, allowing an easy identification of characterizing annotations even in complex experimental settings. Due to the rapidly increasing amount of annotation data available, we apply an annotation filter. Hereby the number of displayed annotations can be significantly reduced to a set of descriptive ones, further enhancing the interpretability of the plot. We validated the method on transcription data from Saccharomyces cerevisiae and human pancreatic adenocarcinomas.


BACKGROUND
The M-CHiPS software is accessible for collaborators at http://www.mchips.org
Busold
Christian H
CH
Division of Functional Genome Analysis, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany. c.busold@dkfz.de
Winter
Stefan
S
Hauser
Nicole
N
Bauer
Andrea
A
Dippon
Jürgen
J
Hoheisel
Jörg D
JD
Fellenberg
Kurt
K
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D003628:Database Management Systems; D030562:Databases, Protein; D004282:Documentation / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D009363:Neoplasm Proteins / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010190:Pancreatic Neoplasms / Q000235:genetics / Q000378:metabolism; D011506:Proteins / Q000032:analysis* / Q000145:classification* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000145:classification / Q000378:metabolism; D018511:Systems Integration
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins