eLSA

eLSA detects local and time-delayed associations in time series biological data by extending Local Similarity Analysis to incorporate replicates for analysis of metagenomics, microbial community, and gene expression datasets.

Key Features:

• Time-Dependent Association Detection: Identifies local and potentially time-delayed co-occurrence and association patterns in time series datasets that are not captured by conventional correlation analysis.
• Handling of Replicates: Incorporates replicates to estimate variability and confidence intervals of Local Similarity (LS) scores for more robust inference.
• Comprehensive Analytic Pipeline: Integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction within the analysis workflow.
• Simulation Validation: Uses simulations to validate detection of subinterval and time-delayed associations in replicated datasets.
• Application to Diverse Biological Data: Applies to metagenomics-derived time series, microbial community datasets, and gene expression datasets to reveal dynamic associations.

Scientific Applications:

• Microbial Ecology: Detects temporal association patterns and potential interactions within microbial community time series derived from metagenomics.
• Gene Expression Dynamics: Reveals time-dependent and delayed associations in gene expression time series to inform dynamic regulatory relationships.

Methodology:

Extends Local Similarity Analysis (LSA) to incorporate replicates, computes Local Similarity (LS) scores, performs data normalization, statistical correlation calculations, statistical significance evaluation, constructs association networks, and validates detections using simulations.