Analyzing single-cell transcriptomes promises to decipher the plasticity, heterogeneity, and rapid switches in developmental cellular state transitions. Such analyses require the identification of gene markers for semi-stable transition states. However, there are nontrivial challenges such as unexplainable stochasticity, variable population sizes, and alternative trajectory constructions. By advancing current tipping-point theory-based models with feature selection, network decomposition, accurate estimation of correlations, and optimization, we developed BioTIP to overcome these challenges. BioTIP identifies a small group of genes, called critical transition signal (CTS), to characterize regulated stochasticity during semi-stable transitions. Although methods rooted in different theories converged at the same transition events in two benchmark datasets, BioTIP is unique in inferring lineage-determining transcription factors governing critical transition. Applying BioTIP to mouse gastrulation data, we identify multiple CTSs from one dataset and validated their significance in another independent dataset. We detect the established regulator Etv2 whose expression change drives the haemato-endothelial bifurcation, and its targets together in CTS across three datasets. After comparing to three current methods using six datasets, we show that BioTIP is accurate, user-friendly, independent of pseudo-temporal trajectory, and captures significantly interconnected and reproducible CTSs. We expect BioTIP to provide great insight into dynamic regulations of lineage-determining factors.

Highlights: We adopt 'tipping-point' theory to identify distribution-transition in disease regulatory states  Critical transcriptional transition happens between low-risk and a high-risk neuroblastoma states  A critical transition signal (CTS) based on coherent expression of genes and lncRNAs shows prognostic significance  GWAS-scans with the CTS unveiled five overlooked genes and four lncRNAs with promising clinical implications  We propose a CTS-amplifier model that unveils complex but mastering transregulation in disease AbstractTipping-point models have had success identifying transcriptional critical-transition signal (CTS) in tumor phenotypes using time-course data. Can these mathematical models be adopted to cross-sectional transcriptome profiles? Furthermore, can the 2 CTS analysis that characterizes tumor progression be applied to lncRNA-expression patterns? This study introduces a novel network-perturbation signature (NPS) scoring scheme to model a phenotype-defined tumor regulatory system. Applying NPS to neuroblastoma transcriptome of two patient populations yielded two CTSs that reproducibly identified a critical system transition between the low-risk and a high-risk state. The coherent expression pattern of one specific CTS, consisting of mRNA and lncRNA components, showed prognostic significance. Associating GWAS-scans with the CTS unveiled four overlooked intergenic loci and five genes with promising clinical significance. Additionally, a new mechanism of 'CTS-amplifier' is proposed, modeling how CTS-transcript fluctuation response to complex master regulators such as c-MYC and HNF4A uniquely in the transition state. Overall, NPS is a powerful computational approach that provides a breakthrough in phenomenological analysis of collective regulatory trajectory by applying 'tipping-point' theory to '-omics' data.