A Bayesian non parametric integration approach for functional regulatory network inference in hematopoietic development from multiomics data

Alcalà Plazas, Laia C.

A Bayesian non parametric integration approach for functional regulatory network inference in hematopoietic development from multiomics data

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Alcalà Plazas, L. A Bayesian non parametric integration approach for functional regulatory network inference in hematopoietic development from multiomics data. 2023. handle: http://hdl.handle.net/10230/58223

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Abstract

We have developed Bayesian non parametric model called Monet (Multi-Omics NETwork inference) in order to infer the TF-gene network interaction and the TF activity strength without knowing a priori the number of participating TF in each stage of a molecular process. Statistical inference is performed in a Bayesian setting using an implementation of HMC-derived NUTS in Stan. Here, we demonstrate the Monet's capability to extract these parameters and we apply it to mouse hematopoietic data to briefly analyze the changes in Sp1 TF knockdown. Our model allows us to make predictions on the importance of specific TF over time and under different perturbation settings.