Data Analysis

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.

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.

An Introduction to the Harrell“verse”: Predictive Modeling using the Hmisc and rms Packages

This is post is to introduce members of the Cincinnati Children’s Hospital Medical Center R Users Group (CCHMC-RUG) to some of the functionality provided by Frank Harrell’s Hmisc and rms packages for data description and predictive modeling.

Introduction to the Statistical Analysis of Microbiome Data in R

This post is also from the Introduction to Metagenomics Summer Workshop and provides a quick introduction to some common analytic methods used to analyze microbiome data. I thought it might be of interest to a broader audience so decided to post it here.