Causality and pathway search in microarray time series experiment

Nitai D. Mukhopadhyay, Snigdhansu Chatterjee

Research output: Contribution to journalArticlepeer-review

96 Scopus citations

Abstract

Motivation: Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities. Results: Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks.

Original languageEnglish (US)
Pages (from-to)442-449
Number of pages8
JournalBioinformatics
Volume23
Issue number4
DOIs
StatePublished - Feb 15 2007

Bibliographical note

Funding Information:
We benefitted from a discussion with Professor Sounak Mishra about construction of minimal spanning trees. The detailed feedback from two anonymous reviewers helped in revising the manuscript. The research of the second author is partially supported by a grant from the University of Minnesota.

Fingerprint

Dive into the research topics of 'Causality and pathway search in microarray time series experiment'. Together they form a unique fingerprint.

Cite this