ProgPerm

ProgPerm applies progressive label permutation to evaluate and identify robust differential microbial features in microbiome datasets.

Key Features:

• Progressive Permutation: ProgPerm progressively permutes grouping-factor labels and conducts multiple differential abundance tests across permutation scenarios to assess feature stability.
• Signal vs. Noise Differentiation: It compares the signal strength of top hits from the original data to their performance in permuted datasets to distinguish true positives from noise.
• U-Curve Visualization: ProgPerm generates a U-curve plotting the number of significant features against the proportion of mixing to summarize the association between the microbiome and the grouping factor.
• Fragility Index: The method computes a fragility index that quantifies how sensitive identified features are to changes in label permutations.
• Flexible Statistical Testing: ProgPerm computes p-values by default using the Wilcoxon rank sum test and supports alternative differential testing methods such as DESeq.

Scientific Applications:

• Robust differential discovery: Assessing and validating differential microbial features in high-dimensional, heterogeneous microbiome studies.
• Association strength assessment: Quantifying and ranking the robustness of microbiome–grouping factor associations for studies comparing conditions or treatments.

Methodology:

Systematic permutation of grouping-factor labels, differential abundance testing across permutations (e.g., Wilcoxon rank sum test or DESeq), comparison of original versus permuted signals, visualization via U-curves, and computation of fragility indices.