Probabilistic retrieval and visualization of biologically relevant microarray experiments.
BACKGROUND
As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.


RESULTS
We use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques.


BACKGROUND
The code is available from http://www.cis.hut.fi/projects/mi/software/ismb09.
Caldas
José
J
Department of Information and Computer Science, Helsinki Institute for Information Technology, Helsinki University of Technology, Finland. jose.caldas@tkk.fi
Gehlenborg
Nils
N
Faisal
Ali
A
Brazma
Alvis
A
Kaski
Samuel
S
D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D016247:Information Storage and Retrieval; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014584:User-Computer Interface