Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R.
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ABSTRACT:
SUBMITTER: Dong Z
PROVIDER: S-EPMC9207570 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
Publications
Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R.
Dong Zhaorui Z Sun Xiaoqiang X
STAR protocols 20220614 3
Due to a lack of explicit temporal information, it can be challenging to infer gene regulatory networks from clinical transcriptomic data. Here, we describe the protocol of PROB_R for inferring latent temporal disease progression and reconstructing gene regulatory networks from cross-sectional clinical transcriptomic data. We illustrate the protocol by applying it to a breast cancer dataset to demonstrate its use in recovering pseudo-temporal dynamics of gene expression alongside disease progres ...[more]
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Project description:MotivationThe ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved 'hypernetwork'. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression.ResultsResults on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase.Availability and implementationMATLAB code is available from http://go.warwick.ac.uk/systemsbiology/software/.
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