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DREAM3: Network Inference Using Dynamic Context Likelihood of Relatedness and the Inferelator

Figure 3

Performance as a function of data set used.

We evaluated the contribution of each data set (namely: knock-down (‘kd’), time-series (‘ts’), knock-out (‘ko’), and all three combined (‘all’)) to performance of CLR, mixed-CLR, Inferelator 1.0, and CLR or mixed-CLR with Inferelator 1.0 (no filtration was used, ). Note, mixed-CLR is a generalization of CLR that takes advantage of time-series data, when time-series data is not used (i.e. for ‘kd’ and ‘ko’) the two are equivalent. For all tested methods ‘ko’ data contributes the most to performance (followed by ‘ts’ and ‘kd’ data respectively). The inclusion of a dynamical model allowed mixed-CLR and Inferelator 1.0 to take advantage of ‘ts’ data (compare to CLR above ‘ts’ and ‘all’ data partitions). Mixed-CLR and Inferelator 1.0 are complimentary, as evidenced by the improvement in performance when the two methods are combined. For ‘ts’, ‘ko’, and ‘all’ data partitions, mixed-CLR with Inferelator 1.0, the method we used to make predictions for DREAM3, gave optimal performance. Error bars for methods involving Inferelator 1.0 are drawn at one standard deviation (estimated from ten Inferelator 1.0 runs).

doi: https://doi.org/10.1371/journal.pone.0009803.g003