PANDA-view

PANDA-view performs statistical analysis and visualization of quantitative proteomics and other -omics data to support downstream interpretation and identification of significant protein-level changes.

Key Features:

• Statistical analysis methods: Implements normalization procedures, missing value imputation, and statistical tests to identify significant changes in protein expression levels.
• Volcano plot visualization: Generates volcano plots that display significance versus fold change for differential expression assessment.
• Clustering and PCA: Provides clustering algorithms and principal component analysis (PCA) to explore patterns and relationships within datasets.
• Multi-level proteomics mapping: Represents quantitative data at protein, peptide, and spectrum levels via protein-peptide-spectrum mappings.
• Support for other -omics data: Handles various types of -omics data beyond proteomics for cross-dataset analysis.

Scientific Applications:

• Biomarker discovery: Identifies candidate biomarkers through statistical testing and visualization of protein-level changes.
• Differential expression analysis: Detects proteins with significant expression differences across conditions using fold change and significance metrics.
• Study of protein interactions and modifications: Facilitates analysis of proteins, their interactions, and modifications via multi-level quantitative mappings.
• Investigation of disease mechanisms: Supports analysis aimed at elucidating molecular changes associated with disease states.

Methodology:

Normalization procedures, missing value imputation, statistical tests for protein expression changes, volcano plot visualization of significance versus fold change, clustering algorithms, principal component analysis (PCA), and protein-peptide-spectrum mappings.