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gene2gauss: A multi-view gaussian gene embedding learner for analyzing transcriptomic networks.

ABSTRACT: Analyzing gene co-expression networks can help in the discovery of biological processes and regulatory mechanisms underlying normal or perturbed states. Unlike standard differential analysis, network-based approaches consider the interactions between the genes involved leading to biologically relevant results. Applying such network-based methods to jointly analyze multiple transcriptomic networks representing independent disease cohorts or studies could lead to the identification of more robust gene modules or gene regulatory networks. We present gene2gauss, a novel feature learning framework that is capable of embedding genes as multivariate gaussian distributions by taking into account their long-range interaction neighborhoods across multiple transcriptomic studies. Using multiple gene co-expression networks from idiopathic pulmonary fibrosis, we demonstrate that these multi-dimensional gaussian features are suitable for identifying regulons of known transcription factors (TF). Using standard TF-target libraries, we demonstrate that the features from our method are highly relevant in comparison with other feature learning approaches on transcriptomic data.

SUBMITTER: Ghandikota S

PROVIDER: S-EPMC9285176 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

<i>gene2gauss</i>: A multi-view gaussian gene embedding learner for analyzing transcriptomic networks.

Ghandikota Sudhir S Jegga Anil G AG

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20220523

Analyzing gene co-expression networks can help in the discovery of biological processes and regulatory mechanisms underlying normal or perturbed states. Unlike standard differential analysis, network-based approaches consider the interactions between the genes involved leading to biologically relevant results. Applying such network-based methods to jointly analyze multiple transcriptomic networks representing independent disease cohorts or studies could lead to the identification of more robust ...[more]

PMID: 35854722

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Project description:BackgroundHigh-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience. Although existing solutions usually provide adequate procedures for normalization, exploration or differential expression, more advanced features, such as gene clustering or regulatory network inference, often miss or do not reflect current state of the art methodologies.ResultsWe developed here a user interface called DIANE (Dashboard for the Inference and Analysis of Networks from Expression data) designed to harness the potential of multi-factorial expression datasets from any organisms through a precise set of methods. DIANE interactive workflow provides normalization, dimensionality reduction, differential expression and ontology enrichment. Gene clustering can be performed and explored via configurable Mixture Models, and Random Forests are used to infer gene regulatory networks. DIANE also includes a novel procedure to assess the statistical significance of regulator-target influence measures based on permutations for Random Forest importance metrics. All along the pipeline, session reports and results can be downloaded to ensure clear and reproducible analyses.ConclusionsWe demonstrate the value and the benefits of DIANE using a recently published data set describing the transcriptional response of Arabidopsis thaliana under the combination of temperature, drought and salinity perturbations. We show that DIANE can intuitively carry out informative exploration and statistical procedures with RNA-Seq data, perform model based gene expression profiles clustering and go further into gene network reconstruction, providing relevant candidate genes or signalling pathways to explore. DIANE is available as a web service ( https://diane.bpmp.inrae.fr ), or can be installed and locally launched as a complete R package.

Dataset Information

gene2gauss: A multi-view gaussian gene embedding learner for analyzing transcriptomic networks.

Publications

<i>gene2gauss</i>: A multi-view gaussian gene embedding learner for analyzing transcriptomic networks.

Similar Datasets

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