R

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

This is a link to a talk I will be giving to the Cincinnati Children’s Hospital Medical Center R Users Group on November 6th, 2019. The goal of the talk is to introduce members to some of the functionality provided by Frank Harrell’s Hmisc and rms …

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. For those of you who have worked with these packages before, hopefully we will cover something new. Dr. Harrell is the founding chair of the Department of Biostatistics at Vanderbilt University (until 2017), Fellow of the American Statistical Association, and a member of the R Foundation.

Denoising Amplicon Sequence Data Using USEARCH and UNOISE3

During the Introduction to Metagenomics Summer Workshop we discussed denoising amplicon sequence variants and worked through Ben Callahan’s DADA2 tutorial. During that session, I mentioned several other approaches and algorithms for denoising or clustering amplicon sequence data including UNOISE3, DeBlur and Mothur. I also mentioned I would try to post some example workflows for some of these other approaches to highlight the similarities, as well as the differences. It looks like I am just now getting around to it.

Introduction to Phyloseq

This post is from a tutorial demonstrating the processing of amplicon short read data in R taught as part of the Introduction to Metagenomics Summer Workshop. It provides a quick introduction some of the functionality provided by phyloseq and follows some of Paul McMurdie’s excellent tutorials. This tutorial picks up where Ben Callahan’s DADA2 tutorial leaves off and highlights some of the main accessor and processor functions of the package.

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. The goal of this session is to provide you with a high-level introduction to some common analytic methods used to analyze microbiome data. It will also serve to introduce you several popular R packages developed specifically for microbiome data analysis.

Introduction to Microbiome Data Analysis

This was a talk given as part of the UC-CCHMC introductory workshop covering the basics of microbial metagenomic sequence data processing and statistical analysis. Topics covered in this talk include an introduction to challenging features of …

Introduction to Denoising Amplicon Sequence Data in R

This was a talk given as part of the UC-CCHMC introductory workshop covering the basics of microbial metagenomic sequence data processing and statistical analysis. Topics covered include an introduction to denoising 16S rRNA gene sequencing data, a …

Recent Advancements in the Statistical Analysis of Microbial Metagenomic Sequence Data

These slides are from an invited talk I gave to the Division of Gastroenterology, Hepatology and Nutrition at CCHMC as part of their Digestive Health Center Seminar Series. Topics covered in this talk include an introduction to denoising amplicon …