IonFlow

IonFlow processes ionomics data from inductively coupled plasma mass spectrometry (ICP-MS) and applies multivariate and network-based analyses to interpret elemental profiles in biological samples.

Key Features:

• ICP-MS support: Handles data generated by inductively coupled plasma mass spectrometry (ICP-MS).
• Raw data processing: Processes raw ionomics datasets into formats suitable for downstream analysis.
• Multivariate statistical techniques: Performs multivariate analyses to identify patterns in multi-dimensional elemental data.
• Principal components analysis (PCA): Implements PCA for dimensionality reduction and visualization.
• Hierarchical clustering: Performs hierarchical clustering to group samples or elemental features.
• Relevance network extraction and analysis: Extracts and analyzes relevance networks to identify element–element associations.
• Gene set enrichment analysis: Conducts gene set enrichment analysis on relevant feature sets.
• Network-based algorithms: Applies network-based algorithms for integrative interpretation of ionomic relationships.
• R package implementation: Implemented as a modification of an R package.

Scientific Applications:

• High-throughput elemental profiling: Analysis of complex multi-dimensional datasets derived from high-throughput elemental profiling experiments.
• Cross-species ionome comparison: Applied to datasets from haploid Saccharomyces cerevisiae (S. cerevisiae) and human HeLa cells for comparative ionomic analysis.
• Elemental composition interpretation: Interpretation of elemental profiles to study biological systems and processes.

Methodology:

Processes raw ionomics data and applies multivariate statistical techniques and network-based algorithms, including principal components analysis (PCA), hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis, to transform raw data into interpretable results.