We've updated our Privacy Policy to make it clearer how we use your personal data.

We use cookies to provide you with a better experience. You can read our Cookie Policy here.

Advertisement
Conceptual Aspects of Large Meta-Analyses with Publicly Available Microarray Data: A Case Study in Oncology
News

Conceptual Aspects of Large Meta-Analyses with Publicly Available Microarray Data: A Case Study in Oncology

Conceptual Aspects of Large Meta-Analyses with Publicly Available Microarray Data: A Case Study in Oncology
News

Conceptual Aspects of Large Meta-Analyses with Publicly Available Microarray Data: A Case Study in Oncology

Read time:
 

Want a FREE PDF version of This News Story?

Complete the form below and we will email you a PDF version of "Conceptual Aspects of Large Meta-Analyses with Publicly Available Microarray Data: A Case Study in Oncology"

First Name*
Last Name*
Email Address*
Country*
Company Type*
Job Function*
Would you like to receive further email communication from Technology Networks?

Technology Networks Ltd. needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, check out our Privacy Policy

Abstract

Large public repositories of microarray experiments offer an abundance of biological data. It is of interest to use and to combine the available material to create new biological information and to develop a broader view on biological phenomena. Meta-analyses recombine similar information over a series of experiments to sketch scientific aspects which were not accessible by each of the single experiments. Meta-analysis of high-throughput experiments has to handle methodological as well as technical challenges. Methodological aspects concern the identification of homogeneous material which can be combined by appropriate statistical procedures. Technical challenges come from the data management of large amounts of high-dimensional data, long computation time, as well as the handling of the stored phenotype data. This paper compares in a meta-analysis of a large series of microarray experiments the interaction structure within selected pathways between different tumour entities. The feasibility of such a study is explored and a technical as well as a statistical framework for its completion is presented. Multiple obstacles were met during completion of this project. They are mainly related to the quality of the available data and influence the biological interpretation of the results derived. The sobering experience of our study asks for combined efforts to improve the data quality in public repositories of high-throughput data. The exploration of the available data in large meta-analyses is limited by incomplete documentation of essential aspects of experiments and studies, by technical deficiencies in the data stored, and by careless duplications of data.

This article published online in the journal Bioinformatics & Biology Insights and is free to access.

Advertisement