Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data

Röth, Volker; Braun, Mikio L.; Lange, Tilman; Buhmann, Joachim M.

doi:10.1007/3-540-46084-5_99

Cited by 11 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The instability index measures the ability of a clustered data set to predict the clustering of another data set sampled from the same source [ 48 ].…”

Section: Validation Indicesmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Algorithms: On Learning, Validation, Performance, and Applications to Genomics

Dalton

Ballarin²,

Brun³

2009

View full text Add to dashboard Cite

The development of microarray technology has enabled scientists to measure the expression of thousands of genes simultaneously, resulting in a surge of interest in several disciplines throughout biology and medicine. While data clustering has been used for decades in image processing and pattern recognition, in recent years it has joined this wave of activity as a popular technique to analyze microarrays. To illustrate its application to genomics, clustering applied to genes from a set of microarray data groups together those genes whose expression levels exhibit similar behavior throughout the samples, and when applied to samples it offers the potential to discriminate pathologies based on their differential patterns of gene expression. Although clustering has now been used for many years in the context of gene expression microarrays, it has remained highly problematic. The choice of a clustering algorithm and validation index is not a trivial one, more so when applying them to high throughput biological or medical data. Factors to consider when choosing an algorithm include the nature of the application, the characteristics of the objects to be analyzed, the expected number and shape of the clusters, and the complexity of the problem versus computational power available. In some cases a very simple algorithm may be appropriate to tackle a problem, but many situations may require a more complex and powerful algorithm better suited for the job at hand. In this paper, we will cover the theoretical aspects of clustering, including error and learning, followed by an overview of popular clustering algorithms and classical validation indices. We also discuss the relative performance of these algorithms and indices and conclude with examples of the application of clustering to computational biology.

show abstract

“…The instability index measures the ability of a clustered data set to predict the clustering of another data set sampled from the same source [ 48 ].…”

Section: Validation Indicesmentioning

confidence: 99%

“…The instability index depends on the number of clusters, and therefore needs to be normalized when used for model selection [ 48 ]. The normalization is obtained by dividing by the instability obtained when using a random estimator as the classifier.…”

Section: Validation Indicesmentioning

confidence: 99%

Clustering Algorithms: On Learning, Validation, Performance, and Applications to Genomics

Dalton

Ballarin²,

Brun³

2009

View full text Add to dashboard Cite

show abstract

“…Here we consider the stability index, which assesses the validity of the partitioning found by clustering algorithms [58,59]. The stability index measures the ability of a clustered data set to predict the clustering of another data set sampled from the same source.…”

Section: Validation Indicesmentioning

confidence: 99%

Validation of Computational Methods in Genomics

Doughtery

Jiang²,

Bittner³

2007

View full text Add to dashboard Cite

High-throughput technologies for genomics provide tens of thousands of genetic measurements, for instance, gene-expression measurements on microarrays, and the availability of these measurements has motivated the use of machine learning (inference) methods for classification, clustering, and gene networks. Generally, a design method will yield a model that satisfies some model constraints and fits the data in some manner. On the other hand, a scientific theory consists of two parts: (1) a mathematical model to characterize relations between variables, and (2) a set of relations between model variables and observables that are used to validate the model via predictive experiments. Although machine learning algorithms are constructed to hopefully produce valid scientific models, they do not ipso facto do so. In some cases, such as classifier estimation, there is a well-developed error theory that relates to model validity according to various statistical theorems, but in others such as clustering, there is a lack of understanding of the relationship between the learning algorithms and validation. The issue of validation is especially problematic in situations where the sample size is small in comparison with the dimensionality (number of variables), which is commonplace in genomics, because the convergence theory of learning algorithms is typically asymptotic and the algorithms often perform in counter-intuitive ways when used with samples that are small in relation to the number of variables. For translational genomics, validation is perhaps the most critical issue, because it is imperative that we understand the performance of a diagnostic or therapeutic procedure to be used in the clinic, and this performance relates directly to the validity of the model behind the procedure. This paper treats the validation issue as it appears in two classes of inference algorithms relating to genomics -classification and clustering. It formulates the problem and reviews salient results.

show abstract

“…According to McShane et al, "Clustering algorithms always detect clusters, even in random data and it is imperative to conduct some statistical assessments of the strength of evidence for any clustering and to examine the reproducibility of individual clusters" [ 3 ]. Roth et al defined stability as "the variability of solutions which are computed from different data sets sampled on the same source" [ 4 ]. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Reproducible Clusters from Microarray Research: Whither?

et al. 2005

View full text Add to dashboard Cite

Motivation: In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets.

show abstract

Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data

Cited by 11 publications

References 5 publications

Clustering Algorithms: On Learning, Validation, Performance, and Applications to Genomics

Clustering Algorithms: On Learning, Validation, Performance, and Applications to Genomics

Validation of Computational Methods in Genomics

Reproducible Clusters from Microarray Research: Whither?

Contact Info

Product

Resources

About