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A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data

Sanchez-Castillo, M, Blanco, D, Tienda-Luna, I M, Carrion, M C, Huang, Yufei, Stegle, Oliver
Bioinformatics 2018 v.34 no.6 pp. 964-970
Bayesian theory, Danio rerio, bioinformatics, data collection, gene regulatory networks, hematopoietic stem cells, mice, models, quantitative polymerase chain reaction, sequence analysis, time series analysis
Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes. We present a novel approach for modelling and inferring gene regulatory networks from high-throughput time series and pseudo-temporally sorted single-cell data. Our method is based on a first-order autoregressive moving-average model and it infers the gene regulatory network within a variational Bayesian framework. We validate our method with synthetic data and we apply it to single cell qPCR and RNA-Seq data for mouse embryonic cells and hematopoietic cells in zebra fish. The method presented in this article is available at