Reverse engineering gene networks using singular value decomposition and robust regression
- Center for BioDynamics and Department of Biomedical Engineering, Boston University, Boston, MA 02215
-
Edited by Charles S. Peskin, New York University, Hartsdale, NY, and approved March 13, 2002 (received for review October 29, 2001)
Abstract
We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses robust regression to identify the solution with the smallest number of connections as the most likely solution. Our algorithm has O(log N) sampling complexity and O(N 4) computational complexity. We test and validate our approach in a series of in numero experiments on model gene networks.
Footnotes
-
↵ † Present address: Stockholm Bioinformatic Center, SCFAB, S-10691 Stockholm, and Department of Numerical Analysis and Computer Science, Royal Institute of Technology, S-10044 Stockholm, Sweden.
-
↵ ‡ To whom reprint requests should be addressed at: Center for BioDynamics and Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215. E-mail: jcollins{at}bu.edu.
-
This paper was submitted directly (Track II) to the PNAS office.
- Abbreviation:
- SVD,
- singular value decomposition
- Copyright © 2002, The National Academy of Sciences
