OMICsPCA

OMICsPCA integrates multi-omics experiments from various subjects, treatments, or time points and analyzes such integrated data from multiple angles and perspectives.

Key Features:

• Multi-omics Integration: OMICsPCA uses Principal Component Analysis (PCA) to integrate multi-omics experiments from various sources.
• Data Insufficiency Handling: The tool addresses data insufficiency issues by using integrated data as representatives.
• Distribution Analysis: OMICsPCA analyzes overall distribution of omics assays across various samples, individuals, or time points.
• Condition-Based Grouping: The tool groups assays by user-defined conditions.
• Variation Source Identification: OMICsPCA identifies sources of variation in integrated datasets.
• Similarity and Dissimilarity Assessment: The tool evaluates similarity and dissimilarity between assays, variables, or individuals.

Scientific Applications:

• Heterogeneous Sample Integration: OMICsPCA integrates and analyzes multiple omics assays from heterogeneous samples including cell lines, individuals, disease or control treatments, and time points.
• Multi-Perspective Data Analysis: The tool enables analysis of integrated multi-omics data from various angles and perspectives.

Methodology:

OMICsPCA uses Principal Component Analysis (PCA) as its core computational method to integrate multi-omics experiments from various sources.