Dataset Information

Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data.

ABSTRACT: BACKGROUND:The association-rules discovery (ARD) technique has yet to be applied to gene-expression data analysis. Even in the absence of previous biological knowledge, it should identify sets of genes whose expression is correlated. The first association-rule miners appeared six years ago and proved efficient at dealing with sparse and weakly correlated data. A huge international research effort has led to new algorithms for tackling difficult contexts and these are particularly suited to analysis of large gene-expression matrices. To validate the ARD technique we have applied it to freely available human serial analysis of gene expression (SAGE) data. RESULTS:The approach described here enables us to designate sets of strong association rules. We normalized the SAGE data before applying our association rule miner. Depending on the discretization algorithm used, different properties of the data were highlighted. Both common and specific interpretations could be made from the extracted rules. In each and every case the extracted collections of rules indicated that a very strong co-regulation of mRNA encoding ribosomal proteins occurs in the dataset. Several rules associating proteins involved in signal transduction were obtained and analyzed, some pointing to yet-unexplored directions. Furthermore, by examining a subset of these rules, we were able both to reassign a wrongly labeled tag, and to propose a function for an expressed sequence tag encoding a protein of unknown function. CONCLUSIONS:We show that ARD is a promising technique that turns out to be complementary to existing gene-expression clustering techniques.

SUBMITTER: Becquet C

PROVIDER: S-EPMC151169 | biostudies-literature | 2002

REPOSITORIES: biostudies-literature

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Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data.

Becquet Céline C Blachon Sylvain S Jeudy Baptiste B Boulicaut Jean-Francois JF Gandrillon Olivier O

Genome biology 20021121 12

<h4>Background</h4>The association-rules discovery (ARD) technique has yet to be applied to gene-expression data analysis. Even in the absence of previous biological knowledge, it should identify sets of genes whose expression is correlated. The first association-rule miners appeared six years ago and proved efficient at dealing with sparse and weakly correlated data. A huge international research effort has led to new algorithms for tackling difficult contexts and these are particularly suited ...[more]

PMID: 12537556

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Project description:As a growing number of complementary transcripts, susceptible to exert various regulatory functions, are being found in eukaryotes, high throughput analytical methods are needed to investigate their expression in multiple biological samples. Serial Analysis of Gene Expression (SAGE), based on the enumeration of directionally reliable short cDNA sequences (tags), is capable of revealing antisense transcripts. We initially detected them by observing tags that mapped on to the reverse complement of known mRNAs. The presence of such tags in individual SAGE libraries suggested that SAGE datasets contain latent information on antisense transcripts. We raised a collection of virtual tags for mining these data. Tag pairs were assembled by searching for complementarities between 24-nt long sequences centered on the potential SAGE-anchoring sites of well-annotated human expressed sequences. An analysis of their presence in a large collection of published SAGE libraries revealed transcripts expressed at high levels from both strands of two adjacent, oppositely oriented, transcription units. In other cases, the respective transcripts of such cis-oriented genes displayed a mutually exclusive expression pattern or were co-expressed in a small number of libraries. Other tag pairs revealed overlapping transcripts of trans-encoded unique genes. Finally, we isolated a group of tags shared by multiple transcripts. Most of them mapped on to retroelements, essentially represented in humans by Alu sequences inserted in opposite orientations in the 3'UTR of otherwise different mRNAs. Registering these tags in separate files makes possible computational searches focused on unique sense-antisense pairs. The method developed in the present work shows that SAGE datasets constitute a major resource of rapidly investigating with high sensitivity the expression of antisense transcripts, so that a single tag may be detected in one library when screening a large number of biological samples.

Project description:Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

Dataset Information

Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data.

Publications

Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data.

Similar Datasets

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