Ontological analysis of gene expression data: current tools, limitations, and open problems

Khatri, Purvesh; Drăghici, Sorin

doi:10.1093/bioinformatics/bti565

Cited by 746 publications

(668 citation statements)

References 33 publications

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“…In biology commonly used ontologies include Gene Ontology (GO) 1 [28] and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) 2 [29]. GoMapMan 3 is an extension of the MapMan [30] ontology for plants used in our experiments.…”

Section: Related Workmentioning

confidence: 99%

Contrast Mining from Interesting Subgroups

Langohr

Podpečan

Petek

et al. 2012

Bisociative Knowledge Discovery

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Abstract. Subgroup discovery methods find interesting subsets of objects of a given class. We propose to extend subgroup discovery by a second subgroup discovery step to find interesting subgroups of objects specific for a class in one or more contrast classes. First, a subgroup discovery method is applied. Then, contrast classes of objects are defined by using set theoretic functions on the discovered subgroups of objects. Finally, subgroup discovery is performed to find interesting subgroups within the two contrast classes, pointing out differences between the characteristics of the two. This has various application areas, one being biology, where finding interesting subgroups has been addressed widely for gene-expression data. There, our method finds enriched gene sets which are common to samples in a class (e.g., differential expression in virus infected versus non-infected) and at the same time specific for one or more class attributes (e.g., time points or genotypes). We report on experimental results on a time-series data set for virus infected potato plants. The results present a comprehensive overview of potato's response to virus infection and reveal new research hypotheses for plant biologists.

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Section: Related Workmentioning

confidence: 99%

Contrast Mining from Interesting Subgroups

Langohr

Podpečan

Petek

et al. 2012

Bisociative Knowledge Discovery

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“…Most GSA methods start with a list of differentially expressed genes, and use contingency statistics to determine if the proportion of genes from a given set is surprisingly high [26]. Gene Set Enrichment Analysis is a popular alternative [27].…”

Section: Integrative Data Analysis For Gene Set Biomarker and Diseasementioning

confidence: 99%

Data Integration and Knowledge Discovery in Life Sciences

Famili

Phan

Fauteux

et al. 2010

Trends in Applied Intelligent Systems

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Abstract. Recent advances in various forms of omics technologies have generated huge amount of data. To fully exploit these data sets that in many cases are publicly available, robust computational methodologies need to be developed to deal with the storage, integration, analysis, visualization, and dissemination of these data. In this paper, we describe some of our research activities in data integration leading to novel knowledge discovery in life sciences. Our multi-strategy approach with integration of prior knowledge facilitates a novel means to identify informative genes that could have been missed by the commonly used methods. Our transcriptomics-proteomics integrative framework serves as a means to enhance the confidence of and also to complement transcriptomics discovery. Our new research direction in integrative data analysis of omics data is targeted to identify molecular associations to disease and therapeutic response signatures. The ultimate goal of this research is to facilitate the development of clinical test-kits for early detection, accurate diagnosis/prognosis of disease, and better personalized therapeutic management.

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“…[1][2][3] IGA identifies differentially expressed genes through a variety of methods and tests the difference of the proportion of differentially expressed genes between all genes and a given gene set. 4,5 GSA directly calculates gene subset scores using various statistical methods and calculates the significance level. 6 The IGA method requires an initial calculation of differentially expressed genes that is influenced by the statistical methods and their thresholds.…”

Section: Introductionmentioning

confidence: 99%

DBGSA: a novel method of distance-based gene set analysis

Wang

et al. 2012

J Hum Genet

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When compared with single gene functional analysis, gene set analysis (GSA) can extract more information from gene expression profiles. Currently, several gene set methods have been proposed, but most of the methods cannot detect gene sets with a large number of minor-effect genes. Here, we propose a novel distance-based gene set analysis method. The distance between two groups of genes with different phenotypes based on gene expression should be larger if a certain gene set is significantly associated with the given phenotype. We calculated the distance between two groups with different phenotypes, estimated the significant P-values using two permutation methods and performed multiple hypothesis testing adjustments. This method was performed on one simulated data set and three real data sets. After a comparison and literature verification, we determined that the gene resampling-based permutation method is more suitable for GSA, and the centroid statistical and average linkage statistical distance methods are efficient, especially in detecting gene sets containing more minor-effect genes. We believe that this distance-based method will assist us in finding functional gene sets that are significantly related to a complex trait. Additionally, we have prepared a simple and publically available Perl and R package

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Ontological analysis of gene expression data: current tools, limitations, and open problems

Cited by 746 publications

References 33 publications

Contrast Mining from Interesting Subgroups

Contrast Mining from Interesting Subgroups

Data Integration and Knowledge Discovery in Life Sciences

DBGSA: a novel method of distance-based gene set analysis

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