Imperial College London
Portrait Picture

DrLeonidChindelevitch

Faculty of Medicine, School of Public Health

Lecturer in Infectious Disease Epidemiology
 
 
 
//

Contact

 

l.chindelevitch Website

 
 
//

Location

 

Sir Michael Uren HubWhite City Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Huang:2012:10.1186/1471-2105-13-46,
author = {Huang, C-L and Lamb, J and Chindelevitch, L and Kostrowicki, J and Guinney, J and DeLisi, C and Ziemek, D},
doi = {10.1186/1471-2105-13-46},
journal = {BMC Bioinformatics},
pages = {46--46},
title = {Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge},
url = {http://dx.doi.org/10.1186/1471-2105-13-46},
volume = {13},
year = {2012}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: Identification of active causal regulators is a crucial problem in understanding mechanism of diseasesor finding drug targets. Methods that infer causal regulators directly from primary data have been proposed andsuccessfully validated in some cases. These methods necessarily require very large sample sizes or a mix ofdifferent data types. Recent studies have shown that prior biological knowledge can successfully boost a method’sability to find regulators.Results: We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detectingactive regulators in disease populations by integrating co-expression analysis and a specific type of literaturederived causal relationships. Instead of investigating the co-expression level between regulators and theirregulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that ourmethod performs very well at recovering even weak regulatory relationships with a low false discovery rate. Usingthree separate real biological datasets we were able to recover well known and as yet undescribed, activeregulators for each disease population. The results are represented as a rank-ordered list of regulators, and revealsboth single and higher-order regulatory relationships.Conclusions: CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevantto a disease population of interest and represent a starting point for further investigation. Our findingsdemonstrate that combining co-expression analysis on regulatee sets with a literature-derived network cansuccessfully identify causal regulators and help develop possible hypothesis to explain disease progression.
AU  - Huang,C-L
AU  - Lamb,J
AU  - Chindelevitch,L
AU  - Kostrowicki,J
AU  - Guinney,J
AU  - DeLisi,C
AU  - Ziemek,D
DO  - 10.1186/1471-2105-13-46
EP  - 46
PY  - 2012///
SN  - 1471-2105
SP  - 46
TI  - Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge
T2  - BMC Bioinformatics
UR  - http://dx.doi.org/10.1186/1471-2105-13-46
UR  - https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-46
UR  - http://hdl.handle.net/10044/1/82856
VL  - 13
ER  -
Download