Differential Abundance

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.

Observation Weights for Differential Abundance of Zero-Inflated Microbiome Data with DESeq2

I recently read through Calgaro et. al. “Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data” where they examined the performance of statistical models developed for bulk RNA (RNA-seq), single-cell RNA-seq (scRNA-seq), and microbial metagenomics to: