Standard Workflow

We designed the BioTIP workflow in the following five steps (Fig 1). In this workflow, two steps (Steps 2 and 5) calculated the random scores from randomly selected genes. In step 2, the distribution of random scores is designed to predict the potential tipping point. The rationale is that random genes can, in cases, capture the “symmetry-breaking destabilization” at tipping points (Mojtahedi 2016). In step 5, the random scores are used to validate the significance of the predicted critical transition signals (CTSs). A CTS measures the loss of resilience of previous states and the gain of instability during transitions (Scheffer, 2012). Because a CTS could be both “regulated” or “chaotic”, the detection of non-random CTSs is important and has successes in development and diseases (Chen, 2012, Sarkar, 2019). The same R function (Table 1) can be used to calculate the random scores, for which we demonstrate the implication of Step 5 in the two following sections.

Fig 1. BioTIP workflow with five key analytical steps. RTF: relative transcript fluctuation; PCC: Pearson correlation coefficient; MCI: Module-Criticality Index; Ic: index of critical transition.

Fig 1. BioTIP workflow with five key analytical steps. RTF: relative transcript fluctuation; PCC: Pearson correlation coefficient; MCI: Module-Criticality Index; Ic: index of critical transition.