Dataset Information

Dissecting systems-wide data using mixture models: application to identify affected cellular processes.

ABSTRACT: BACKGROUND: Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives. RESULTS: We developed an analytical method to determine a p-value threshold from a microarray experiment that is dependent on the quality and design of the data set. To this aim, populations of p-values are modeled as mathematical functions in which the parameters to describe these functions are estimated in an unsupervised manner. The strength of the method is exemplified by its application to a published gene expression data set of sporadic and familial breast tumors with BRCA1 or BRCA2 mutations. CONCLUSION: We present an objective and unsupervised way to set thresholds adapted to the quality and design of the experiment. The resulting mathematical description of the data sets of genome-scale experiments enables a probabilistic approach in systems biology.

SUBMITTER: Svensson JP

PROVIDER: S-EPMC1189081 | biostudies-other | 2005

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Dissecting systems-wide data using mixture models: application to identify affected cellular processes.

Svensson J Peter JP de Menezes Renée X RX Turesson Ingela I Giphart-Gassler Micheline M Vrieling Harry H

BMC bioinformatics 20050714

<h4>Background</h4>Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives.<h4>Results</h4>We developed an analytical method to determine a p-value threshold from a microarray experiment that is depende ...[more]

PMID: 16018805

Similar Datasets

Project description:BackgroundElucidating the sequence of molecular events underlying breast cancer formation is of enormous value for understanding this disease and for design of an effective treatment. Gene expression measurements have enabled the study of transcriptome-wide changes involved in tumorigenesis. This usually occurs through identification of differentially expressed genes or pathways.ResultsWe propose a novel approach that is able to delineate new cancer-related cellular processes and the nature of their involvement in tumorigenesis. First, we define modules as densely interconnected and functionally enriched areas of a Protein Interaction Network. Second, 'differential expression' and 'differential co-expression' analyses are applied to the genes in these network modules, allowing for identification of processes that are up- or down-regulated, as well as processes disrupted (low co-expression) or invoked (high co-expression) in different tumor stages. Finally, we propose a strategy to identify regulatory miRNAs potentially responsible for the observed changes in module activities. We demonstrate the potential of this analysis on expression data from a mouse model of mammary gland tumor, monitored over three stages of tumorigenesis. Network modules enriched in adhesion and metabolic processes were found to be inactivated in tumor cells through the combination of dysregulation and down-regulation, whereas the activation of the integrin complex and immune system response modules is achieved through increased co-regulation and up-regulation. Additionally, we confirmed a known miRNA involved in mammary gland tumorigenesis, and present several new candidates for this function.ConclusionsUnderstanding complex diseases requires studying them by integrative approaches that combine data sources and different analysis methods. The integration of methods and data sources proposed here yields a sensitive tool, able to pinpoint new processes with a role in cancer, dissect modulation of their activity and detect the varying assignments of genes to functional modules over the course of a disease.

Dataset Information

Dissecting systems-wide data using mixture models: application to identify affected cellular processes.

Publications

Dissecting systems-wide data using mixture models: application to identify affected cellular processes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets