Experimental guidance for discovering genetic networks through hypothesis reduction on time series
Fig 1
Schematic of the three stages of the Inherent dynamics pipeline in which each step uses different features of the input gene expression time series data.
In the node finding step, each gene expression time trace is used independently to score the strength of periodicity. Genes with stronger periodicity and higher amplitude are hypothesized to be part of the core network and are correspondingly ranked higher. Top ranked nodes are passed into the edge finding step, where time series are used in pairs to score the likelihood of a positive or negative regulatory event in one or the other direction. A ranking is determined using high to low likelihoods and top ranked edges are passed to the network finding step, where subsets of gene expression data consisting of three or more time traces are compared to the global dynamics of network models to produce top ranking networks that are consistent with the order of peaks and troughs across the time series. Statistics of these top ranked networks are used to suggest experimental intervention at the node level.