Network Interpretation and Analysis

Network Interpretation and Analysis#

Overview#

This chapter explains how to interpret the results from previous chapters and different graphical lasso methods implemented in q2-gglasso. We’ll compare three key approaches: Single Graphical Lasso (SGL), Sparse + Low-Rank (SLR), and Adaptive Graphical Lasso.

Graphical Lasso Solutions Comparison

Figure 1: Comparison of network structures obtained from different graphical lasso methods on the Atacama Desert microbiome dataset.

Key Findings#

  • Environmental mediation: Some apparent microbial correlations are mediated by environmental factors (elevation, pH, soil humidity, temperature)

  • Direct interactions: Genuine microbial associations remain significant after controlling for environmental variables

  • Latent structure: The low-rank component captures systematic variation potentially from unmeasured factors or global environmental gradients

Interpretation guidelines:#

  • Compare SGL vs. SLR to distinguish direct from latent-mediated associations.

  • Use adaptive results to identify environment-independent microbial interactions.

  • Consider edge weights and stability across different λ values for robust associations.

When to Use Each Method#

Method

Use When

Key Benefits

SGL

Exploratory analysis
No environmental data
Comprehensive associations
Speed priority

Fast, complete network view

SLR

Suspected confounders
Separate direct/indirect effects
Batch effects present
Core interactions focus

Isolates direct associations

Adaptive model

Environmental data available
Prior knowledge exists
Environment-independent focus
Hypothesis-driven analysis

Uses prior knowledge, controls confounders

Next Steps#

Once network associations are identified, use q2-classo for regression and classification tasks:

  1. Feature selection: Use identified microbial associations as candidate features for environmental or phenotype prediction

  2. Regression analysis: Model continuous outcomes with sparse microbial predictorss

  3. Classification tasks: Predict binary outcome using selected microbial features

  4. Model selection: Validate predictive performance using classo’s built-in model selection methods

Contents