Plant PhysioSpace

Plant PhysioSpace maps experimental gene expression profiles onto physiology-specific patterns derived from a compendium of public transcriptomic datasets to quantify cross-species and cross-platform stress-response similarity.

Key Features:

• Physiology-specific signatures: Extracts physiologically relevant signatures from a collection of public transcriptomic datasets.
• Cross-species and cross-platform mapping: Computes experimental conditions across species and platforms without prior reduction of reference information to specific gene sets.
• Integration of heterogeneous data: Integrates and transforms heterogeneous reference gene expression data into physiology-specific patterns.
• Quantitative similarity scoring: Maps new experimental gene expression data onto physiology-specific patterns and produces quantitative similarity scores.
• Avoidance of gene-wise dimension reduction: Operates without performing gene-wise dimension reduction to identify marker genes or predefined gene sets.
• Robustness to noise and platform bias: Provides measures that are robust to experimental noise and platform-specific biases, including comparisons involving single-cell technologies.
• Characterization of organismal fates: Enables characterization of organismal fates and separation of generic versus specific biological responses within a broad experimental context.
• Software implementation: Implemented in two R packages (a software package and a data package) with precomputed models for four species.

Scientific Applications:

• Cross-species stress-response translation: Translate and compare stress responses across different plant species using a common physiology-centered reference.
• Cross-platform comparison: Compare experiments across platforms, including bulk and single-cell technologies, using a unified pattern space.
• Discrimination of response specificity: Distinguish generic physiological responses from species- or context-specific responses in transcriptomic experiments.
• Context-aware interpretation: Preserve confounders and contextual information to improve interpretation of experimental results.
• Plant stress biology and resilience studies: Support investigations into stress mechanisms and factors relevant to crop resilience by providing comparative quantitative measures.

Methodology:

Extract physiologically relevant signatures from public transcriptomic datasets; integrate and transform heterogeneous reference gene expression data into physiology-specific patterns; map new experimental gene expression onto those patterns to compute quantitative similarity scores, without performing prior gene-wise dimension reduction to predefined gene sets.