| Item type: | Article | ||||||||||||||||||
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| Journal or Publication Title: | Bioinformatics | ||||||||||||||||||
| Publisher: | Oxford Univ. Press | ||||||||||||||||||
| Volume: | 23 | ||||||||||||||||||
| Number of Issue or Book Chapter: | 17 | ||||||||||||||||||
| Page Range: | pp. 2256-2264 | ||||||||||||||||||
| Date: | September 2007 | ||||||||||||||||||
| Institutions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) Informatics and Data Science > Department Computational Life Science > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) | ||||||||||||||||||
| Identification Number: |
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| Classification: |
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| Dewey Decimal Classification: | 500 Science > 570 Life sciences 600 Technology > 610 Medical sciences Medicine | ||||||||||||||||||
| Status: | Published | ||||||||||||||||||
| Refereed: | Yes, this version has been refereed | ||||||||||||||||||
| Created at the University of Regensburg: | No | ||||||||||||||||||
| Item ID: | 30682 |
Abstract
MOTIVATION: Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering, however, can also stratify patients by similarity of their gene expression profiles, thereby defining novel disease entities based on molecular characteristics. Several distance-based cluster algorithms have been ...
Abstract
MOTIVATION:
Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering, however, can also stratify patients by similarity of their gene expression profiles, thereby defining novel disease entities based on molecular characteristics. Several distance-based cluster algorithms have been suggested, but little attention has been given to the distance measure between patients. Even with the Euclidean metric, including and excluding genes from the analysis leads to different distances between the same objects, and consequently different clustering results.
RESULTS:
We describe a new clustering algorithm, in which gene selection is used to derive biologically meaningful clusterings of samples by combining expression profiles and functional annotation data. According to gene annotations, candidate gene sets with specific functional characterizations are generated. Each set defines a different distance measure between patients, leading to different clusterings. These clusterings are filtered using a resampling-based significance measure. Significant clusterings are reported together with the underlying gene sets and their functional definition.
CONCLUSIONS:
Our method reports clusterings defined by biologically focused sets of genes. In annotation-driven clusterings, we have recovered clinically relevant patient subgroups through biologically plausible sets of genes as well as new subgroupings. We conjecture that our method has the potential to reveal so far unknown, clinically relevant classes of patients in an unsupervised manner.
AVAILABILITY:
We provide the R package adSplit as part of Bioconductor release 1.9 and on http://compdiag.molgen.mpg.de/software.
Metadata last modified: 29 Sep 2021 07:40
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