TY - JOUR T1 - State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development JF - bioRxiv DO - 10.1101/238923 SP - 238923 AU - Russell C. Rockne AU - Sergio Branciamore AU - Jing Qi AU - David Frankhouser AU - Denis O’Meally AU - Wei-Kai Hua AU - Guerry J. Cook AU - Emily Carnahan AU - Lianjun Zhang AU - Ayelet Marom AU - Herman Wu AU - Davide Maestrini AU - Xiwei Wu AU - Yate-Ching Yuan AU - Zheng Liu AU - Leo D. Wang AU - Stephen J. Forman AU - Nadia Carlesso AU - Ya-Huei Kuo AU - Guido Marcucci Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/06/04/238923.abstract N2 - Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensional temporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identified step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.Graphical Abstract In brief The theory of state-transition is applied to acute myeloid leukemia (AML) to model transcriptome dynamics and trajectories in a state-space, and is used to identify critical points corresponding to critical transcriptomic perturbations that predict leukemia development.HighlightsLeukemia transcriptome dynamics are modeled as movement in transcriptome state-spaceState-transition model and critical points accurately predicts leukemia developmentCritical point-based approach identifies step-wise transcriptome events in leukemiaState-based geometric analysis provides quantification of leukemogenic contribution ER -