Differential Abundance

Microbiome AB/BA Crossover Design

The motivation for this post was also recent question on twitter asking how one might approach the analysis of an AB/BA crossover study when the goal is to estimate the difference in the relative abundance of microbial taxa under treatments A and B.

Multiple Imputation with Microbiome Data

The motivation for this post was a recent twitter exchange discussing difficulties faced when analyzing microbiome data with missing covariate values (i.e., incomplete sample metadata fields). I have run into this issue as well, and given the increasing size of microbiome studies, expect so to have many others.

Applying Topic Models to Microbiome Data in R

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.

Sample Size Considerations for Microbiome One-at-a-Time Differential Abundance Testing

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.

ANCOVA for Analyzing Pre-Post Microbiome Studies

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.

Differential Feature Abundance Meta-Analysis

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.

Bootstrap Resampling for Ranking Differentially Abundant Taxa

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.

Wrench Normalization for Sparse Microbiome Data

I recently attended a seminar where Wrench normalization was shown to have good performance in the presented simulation studies. So I thought I would give it another look.

Identifying Differentially Abundant Features in Microbiome Data

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.