A model selection criterion for model-based clustering of annotated gene expression data
نویسندگان
چکیده
منابع مشابه
A model selection criterion for model-based clustering of annotated gene expression data.
In co-expression analyses of gene expression data, it is often of interest to interpret clusters of co-expressed genes with respect to a set of external information, such as a potentially incomplete list of functional properties for which a subset of genes may be annotated. Based on the framework of finite mixture models, we propose a model selection criterion that takes into account such exter...
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Model-based clustering and data transformations for gene expression data
MOTIVATION Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying pro...
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Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data
The concept of cluster stability is introduced to assess the validity of data partitionings found by clustering algorithms. It allows us to explicitly quantify the quality of a clustering solution, without being dependent on external information. The principle of maximizing the cluster stability can be interpreted as choosing the most self-consistent data partitioning. We present an empirical e...
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ژورنال
عنوان ژورنال: Statistical Applications in Genetics and Molecular Biology
سال: 2015
ISSN: 1544-6115,2194-6302
DOI: 10.1515/sagmb-2014-0095