EcoPLOT

EcoPLOT performs integrative analysis of multivariate biogeochemical, environmental, geochemical, and microbiome datasets to identify drivers of plant, microbial, and soil dynamics.

Key Features:

• Parameterized Linkage of Omics-Driven Technologies: The name expansion emphasizes integration of omics-driven data within the EcoPLOT framework.
• Multivariate biogeochemical data integration: Supports analysis of environmental, geochemical, and microbiome datasets in a combined framework.
• Statistical and graphical exploration: Provides statistical analyses and graphical outputs for examining complex dataset structure and relationships.
• Iterative Random Forest (iRF) algorithm: Implements the iterative random forest machine learning technique for robust variable selection and pattern discovery.
• De novo driver discovery: Enables identification of significant drivers impacting plant, microbial, and soil dynamics from input datasets.
• R implementation: Constructed entirely in the R programming language for computational analyses.
• Large-variable handling: Uses methods (iRF) suited to datasets with large numbers of variables.

Scientific Applications:

• Driver identification: Detects significant biotic and abiotic drivers of plant, microbial, and soil dynamics.
• Biogeochemical interaction analysis: Examines relationships and interactions within biogeochemical and geochemical processes.
• Microbiome-environment integration: Integrates microbiome data with environmental and geochemical measurements to uncover interdisciplinary patterns.

Methodology:

Analyses are implemented in R and employ statistical and graphical methods plus the iterative random forest algorithm for de novo discovery of significant drivers in multivariate environmental, geochemical, and microbiome datasets.