A Data Integration Methodology for Systems Biology

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make networ...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2005-11, Vol.102 (48), p.17296-17301
Main Authors: Daehee Hwang, Rust, Alistair G., Stephen Ramsey, Smith, Jennifer J., Deena M. Leslie, Weston, Andrea D., Pedro de Atauri, Aitchison, John D., Hood, Leroy, Siegel, Andrew F., Bolouri, Hamid
Format: Article
Language:English
Subjects:
Biological Sciences
Biology
Data integration
Datasets
Error rates
False negative errors
False positive errors
Genes
Informatics - methods
Information retrieval noise
Information Systems
Models, Theoretical
P values
Research methodology
Software
Statistics
Systems biology
Systems Biology - methods
Technology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0508647102