In:
Bioinformatics, Oxford University Press (OUP), Vol. 32, No. 9 ( 2016-05-01), p. 1388-1394
Kurzfassung:
Motivation: Public and private repositories of experimental data are growing to sizes that require dedicated methods for finding relevant data. To improve on the state of the art of keyword searches from annotations, methods for content-based retrieval have been proposed. In the context of gene expression experiments, most methods retrieve gene expression profiles, requiring each experiment to be expressed as a single profile, typically of case versus control. A more general, recently suggested alternative is to retrieve experiments whose models are good for modelling the query dataset. However, for very noisy and high-dimensional query data, this retrieval criterion turns out to be very noisy as well. Results: We propose doing retrieval using a denoised model of the query dataset, instead of the original noisy dataset itself. To this end, we introduce a general probabilistic framework, where each experiment is modelled separately and the retrieval is done by finding related models. For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples. The suggested metric for retrieval using clusterings is the normalized information distance. Empirical results finally suggest that inference for the full probabilistic model can be approximated with good performance using computationally faster heuristic clustering approaches (e.g. k-means). The method is highly scalable and straightforward to apply to construct a general-purpose gene expression experiment retrieval method. Availability and implementation: The method can be implemented using standard clustering algorithms and normalized information distance, available in many statistical software packages. Contact: paul.blomstedt@aalto.fi or samuel.kaski@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online.
Materialart:
Online-Ressource
ISSN:
1367-4811
,
1367-4803
DOI:
10.1093/bioinformatics/btv762
Sprache:
Englisch
Verlag:
Oxford University Press (OUP)
Publikationsdatum:
2016
ZDB Id:
1468345-3
SSG:
12
