Inherent limitations with one-at-a-time (OaaT) feature testing (i.e., single feature differential abundance analysis) have contributed to the increasing popularity of mixture models for correlating microbial features with factors of interest (i.
One of the most common questions we get from investigators at the Microbial Metagenomics Analysis Center (MMAC) is how many samples should I collect for my study? Even once we have a clearly stated and testable hypothesis, this is not always easy, since sample size calculations for microbiome studies are typically not amenable to closed form solutions (i.
I was recently asked how many samples we might need to formally power a differential abundance analysis of prevalent species in the human stool microbiome between “otherwise healthy” control samples and patients with a specific disease condition.
I recently analyzed some data from an experiment with a pre-post study design where our estimands of interest were the baseline adjusted mean difference in Shannon diversity and species differential abundance between arms at post-treatment.
I was thinking recently about the performance of linear models for differential abundance testing in microbiome studies (see here for an example) despite the sparse, extremely non-normal, and compositional nature of mixed microbial community taxonomic profiles.
In a previous post, I highlighted some difficulties one faces with differential abundance (DA) testing when trying to identify those features (i.e., OTUs/ASVs, species, pathways, etc.) that “differ the most” according to some condition of interest.
Microbiome studies often seek to identify individual features (i.e., OTUs/ASVs, species, pathways, etc.) associated some condition (i.e., exposure, experimental treatment, etc.) of interest. This problem can be approached in many different ways, but most commonly, one-at-a-time (OaaT) feature screening is undertaken.
A common goal in many microbiome studies is to identify features (i.e., species, OTUs, gene families, etc.) that differ according to some study condition of interest. While often done, this is a difficult task, and in the Introduction to the Statistical Analysis of Microbiome Data in R post I touch on some of the reasons for this.
Below I provide scripts to implement the current default workflow for taxonomic and functional profiling using the Huttenhower Lab’s Biobakery Tool Suite used by the Microbial Metagenomics Analysis Center (MMAC) at CCHMC for paired-end data.