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Stability of ranked gene lists in large microarray analysis studies.

ABSTRACT: This paper presents an empirical study that aims to explain the relationship between the number of samples and stability of different gene selection techniques for microarray datasets. Unlike other similar studies where number of genes in a ranked gene list is variable, this study uses an alternative approach where stability is observed at different number of samples that are used for gene selection. Three different metrics of stability, including a novel metric in bioinformatics, were used to estimate the stability of the ranked gene lists. Results of this study demonstrate that the univariate selection methods produce significantly more stable ranked gene lists than the multivariate selection methods used in this study. More specifically, thousands of samples are needed for these multivariate selection methods to achieve the same level of stability any given univariate selection method can achieve with only hundreds.

SUBMITTER: Stiglic G

PROVIDER: S-EPMC2896709 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Stability of ranked gene lists in large microarray analysis studies.

Stiglic Gregor G Kokol Peter P

Journal of biomedicine & biotechnology 20100627

This paper presents an empirical study that aims to explain the relationship between the number of samples and stability of different gene selection techniques for microarray datasets. Unlike other similar studies where number of genes in a ranked gene list is variable, this study uses an alternative approach where stability is observed at different number of samples that are used for gene selection. Three different metrics of stability, including a novel metric in bioinformatics, were used to e ...[more]

PMID: 20625502

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Project description:Computational methods for discovery of sequence elements that are enriched in a target set compared with a background set are fundamental in molecular biology research. One example is the discovery of transcription factor binding motifs that are inferred from ChIP-chip (chromatin immuno-precipitation on a microarray) measurements. Several major challenges in sequence motif discovery still require consideration: (i) the need for a principled approach to partitioning the data into target and background sets; (ii) the lack of rigorous models and of an exact p-value for measuring motif enrichment; (iii) the need for an appropriate framework for accounting for motif multiplicity; (iv) the tendency, in many of the existing methods, to report presumably significant motifs even when applied to randomly generated data. In this paper we present a statistical framework for discovering enriched sequence elements in ranked lists that resolves these four issues. We demonstrate the implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences. We applied DRIM to ChIP-chip and CpG methylation data and obtained the following results. (i) Identification of 50 novel putative transcription factor (TF) binding sites in yeast ChIP-chip data. The biological function of some of them was further investigated to gain new insights on transcription regulation networks in yeast. For example, our discoveries enable the elucidation of the network of the TF ARO80. Another finding concerns a systematic TF binding enhancement to sequences containing CA repeats. (ii) Discovery of novel motifs in human cancer CpG methylation data. Remarkably, most of these motifs are similar to DNA sequence elements bound by the Polycomb complex that promotes histone methylation. Our findings thus support a model in which histone methylation and CpG methylation are mechanistically linked. Overall, we demonstrate that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications ranging from expression and ChIP-chip to CpG methylation data. DRIM is publicly available at http://bioinfo.cs.technion.ac.il/drim.

Dataset Information

Stability of ranked gene lists in large microarray analysis studies.

Publications

Stability of ranked gene lists in large microarray analysis studies.

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