Regulatory Impact Factor (RIF) analysis

The purpose of RIF is to identify regulatory molecules whose activity can change independent of any change in gene expression level. This includes Transcription Factors whose activity may be influenced by ligand binding, co-factor binding, cellular localisation and other post-translational processes. The method works by establishing different patterns of network connectivity in the two states (here HFE versus LFE). RIF1 and RIF2 are two versions of essentially the same analysis. In both cases the abundance and differential expression of the ‘target genes’ is exploited in conjunction with the differential co-expression of the ‘regulators’ to those ‘targets.’ The output of both versions has been plotted and reported here.

We computed RIF1 and RIF2 as previously described [22–24]. This procedure exploits global patterns of differential co-expression (or ‘differential wiring’) to infer those regulatory molecules whose behaviour is systematically different in a contrast of interest, in this case the HFE versus LFE birds. Herein, the experimental contrast was HFE vs. LFE and the RIF metrics for the r-th regulator (r = 1, 2, …898) were computed using the following formulae:

and

where n_DE represented the number of DE genes; x _j was the average expression of the j-th DE gene across all time points; d _j was the DE of the j-th gene in the HFE vs. LFE contrast; DC_rj was the differential co-expression between the r-th regulator and the j-th DE gene, and computed from the difference between r _rj^HFE and r _rj^LFE, the correlation co-expression between the r-th regulator and the j-th DE gene in the HFE and LFE samples, respectively; finally, x _j^HFE and x _j^LFE represented the average expression of the j-th DE or TS gene in the HFE and LFE samples, respectively. The RIF1 and RIF2 output were simultaneously plotted (Fig. 4).

The extremely differentially connected TF as illustrated by RIF1 and RIF2 scores. Progesterone receptor and 2 other TF involved in progesterone signalling are awarded extreme scores by both metrics