3D Gene Expression

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Nonparametric Identification of Regulatory Interactions (NODE)

This method provides a quasi-genetic, formal model of regulatory networks, which takes transcription factor protein data and correlates it to the temporal change in mRNA expression of putative target genes locally for clusters of cells throughout the embryo. The outputs are spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. Strong positive correlations suggest a (direct or indirect) activation of the target and strong negative correlations suggest a (direct or indirect) repression of the target.

The method uses nonparametric statistics to generate ordinary differential equation (ODE) models, which are fit using the nonparametric exterior derivative estimator (NEDE). For these reasons, we call our method and the resulting model the NODE (an amalgamation of NEDE and ODE) model. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting.

The download link provides code and a test data set. Further explanation of the method and an example of its use are provided in Aswani et al, 2010.