Text S1 - Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles

Mathematical basics of the prior distributions for initial state, state-transition and emission parameters and details of the chosen prior hyperparameters are given in the section 'Appendix A: Prior distributions’. A detailed derivation of the Bayesian Baum-Welch algorithm for autoregressive higher-order HMMs is given in the section ‘Appendix B: Bayesian Baum-Welch algorithm’. An in-depth analysis of identified hotspots of differential expression in gliomas is given in the section ‘Appendix C: Application of Autoregressive Higher-Order Hidden Markov Models to glioma data’. The supporting Figures S1–S8 are provided in the section ‘Supporting Figures’. The supporting Tables S1–S4 are given in the section ‘Supporting Tables’. (PDF)