Time Series Network Identification TSNI
The manuscript describing TSNI can be downloaded here:
- Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Mukesh Bansal, Giusy Della Gatta, Diego di Bernardo. Bioinformatics -- September 2006 -- 22(7), p. 815-822 Abstract
- Direct targets of the Trp63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Giusy Della Gatta, Mukesh Bansal, Alberto Ambesi-Impiombato, Dario Antonini, Caterina Missero, Diego di Bernardo. Genome Research, 2008 abstract - PDF.
TSNI matlab code can be downloaded from here.
Below the command-line to run TSNI and a short description of its arguments:
[A,B,total_states] = tsni(data,time_points,perturbation,principle_components)
data: no_genes X time_points format, in which first time points is scaled to zero.
time_points: Time points at which measurement are done. It is a row vector.
perturbation: It is a row vector and it is an input to the system. Size is 1 X no_time_points.
principle_components: Number of principle componets to use. If you dont know how many principle components to use, put it to 0.
A: Recovered network. It is a fully connected network.
B: Targets of perturbations. It is the score of each gene to be the target of perturbation.
total_states: Number of principle components used.
% There is a parameter in code which is smoothing parameter. Default % value is 0.8. It can be changed to see how the profiles of the gene % changes. It shouldn't be too low.