Plant PhysioSpace: a robust tool to compare stress response across plant species

Author:

Hadizadeh Esfahani Ali1ORCID,Maß Janina2ORCID,Hallab Asis2,Schuldt Bernhard M3,Nevarez David1,Usadel Björn2ORCID,Ott Mark-Christoph4,Buer Benjamin4ORCID,Schuppert Andreas1

Affiliation:

1. Joint Research Center for Computational Biomedicine, RWTH Aachen University, Aachen 52074, Germany

2. IBG-4: Bioinformatics, Forschungszentrum Jülich, Jülich 52425, Germany

3. Mathematische Modellierung, Düsseldorf, Germany

4. Crop Science Division, Bayer AG, Monheim am Rhein 40789, Germany

Abstract

Abstract Generalization of transcriptomics results can be achieved by comparison across experiments. This generalization is based on integration of interrelated transcriptomics studies into a compendium. Such a focus on the bigger picture enables both characterizations of the fate of an organism and distinction between generic and specific responses. Numerous methods for analyzing transcriptomics datasets exist. Yet, most of these methods focus on gene-wise dimension reduction to obtain marker genes and gene sets for, for example, pathway analysis. Relying only on isolated biological modules might result in missing important confounders and relevant contexts. We developed a method called Plant PhysioSpace, which enables researchers to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene sets. Plant PhysioSpace extracts physiologically relevant signatures from a reference dataset (i.e. a collection of public datasets) by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns. New experimental data can be mapped to these patterns, resulting in similarity scores between the acquired data and the extracted compendium. Because of its robustness against platform bias and noise, Plant PhysioSpace can function as an inter-species or cross-platform similarity measure. We have demonstrated its success in translating stress responses between different species and platforms, including single-cell technologies. We have also implemented two R packages, one software and one data package, and a Shiny web application to facilitate access to our method and precomputed models.

Funder

Bayer AG

Deutsche Forschungsgemeinschaft

Publisher

Oxford University Press (OUP)

Subject

Plant Science,Genetics,Physiology

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