Commonly used statistical methods for analyzing microbiome data can lead to false discoveries
In short: Researchers using simulations and empirical data found that the standard practice of 'rarefying' microbiome counts is statistically flawed. This common technique for normalizing DNA sequencing data often results in a high rate of false positives when trying to identify species that differ between groups, and it can even lead to the unnecessary loss of useful samples during clustering.
The study used simulations and empirical data to compare rarefying and simple proportions against more advanced mixture models. While the researchers demonstrated that models like Negative Binroll or metagenomeSeq offer substantial improvements, they noted that metagenomeSeq can still produce higher false positive rates. The findings suggest that while better alternatives exist, the accuracy of these newer methods still depends on having enough biological replicates.
Why it matters: Researchers should avoid rarefying microbiome data in favor of more robust statistical models to prevent false discoveries.
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Sources
- Source: Research: McMurdie and Holmes (2014). Waste not, want not: why rarefying microbiome data is inadmissible. PLoS computational biology. · cc by
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