Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Area of Science:

• Microbial ecology
• Bioinformatics
• Statistical modeling

Background:

• Correlation analysis is common in microbiome studies to find relationships between bacterial taxa.
• Existing statistical methods face challenges with microbial presence-absence data, particularly concerning rare species.
• Standard metrics like Pearson's correlation coefficient (r) and Jaccard's index (J) can yield conflicting results.

Purpose of the Study:

• To highlight statistical issues in microbiome correlation analysis using presence-absence data.
• To compare the performance of Pearson's correlation coefficient (r) and Jaccard's index (J).
• To introduce an improved statistical method for analyzing microbial community correlations.

Main Methods:

• Analysis of microbiome presence-absence data using Pearson's correlation coefficient (r) and Jaccard's index (J).
• Evaluation of metric performance on a dataset with a high prevalence of rare species.
• Development and application of a null model based on hypergeometric distribution to correct for species prevalence.

Main Results:

• Pearson's r and Jaccard's J showed significant discrepancies in predicting species-pair correlations (e.g., 14% vs. 37.4% mismatch).
• Discrepancies were more pronounced for species-pairs involving rare taxa, common in microbiome datasets.
• Pearson's r can artificially inflate positive taxon relationships, while Jaccard's index with a flawed null model also leads to spurious conclusions.
• A hypergeometric null model provides a robust correction for species prevalence in Jaccard's index calculations.

Conclusions:

• Standard correlation methods (r, J) are unreliable for microbiome presence-absence data due to conflicts and issues with rare species.
• A hypergeometric-based null model offers a statistically sound approach for evaluating Jaccard's index in microbial ecology.
• This improved method enables robust identification of relationships and shared ecological niches among microbial taxa.