Abstract
Motivation: Coexpression networks have recently emerged as a novel
holistic approach to microarray data analysis and interpretation. Choosing an
appropriate cut-off threshold, above which a gene-gene interaction is
considered as relevant, is a critical task in most networkcentric
applications, especially when two or more networks are being compared.
Results: We demonstrate that the performance of traditional approaches, which
are based on a pre-defined cut-off or significance level, can vary drastically
depending on the type of data and application. Therefore, we introduce a
systematic procedure for estimating a cutoff threshold of coexpression
networks directly from their topological properties. Both synthetic and real
datasets show clear benefits of our data-driven approach under various
practical circumstances. In particular, the procedure provides a robust
estimate of individual degree distributions, even from multiple microarray
studies performed with different array platforms or experimental designs,
which can be used to discriminate the corresponding phenotypes. Application to
human T helper cell differentiation process provides useful insights into the
components and interactions controlling this process, many of which would
have remained unidentified on the basis of expression change alone. Moreover,
several human-mouse orthologs showed conserved topological changes in both
systems, suggesting their potential importance in the differentiation process.
Original language | English |
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Pages (from-to) | 2096-2103 |
Journal | Bioinformatics |
Volume | 23 |
Issue number | 16 |
DOIs | |
Publication status | Published - 2007 |
MoE publication type | A1 Journal article-refereed |
Keywords
- gene expression
- genes
- coexpression networks
- cell differentiation
- T helper cell
- transcriptomics