The publicly available data used in this session are from Giloteaux et. In cases where I focus largely on more basic implementations, I have tried to provide links for advanced learning of more complex topics. I also try to show a few different approaches in each section. I have tried to focus on methods that are common in the microbiome literature, well-documented, and reasonably accessible…and a few I think are new and interesting. The statistical analysis of microbial metagenomic sequence data is a rapidly evolving field and different solutions (often many) have been proposed to answer the same questions. However, I try to provide links to source materials and more detailed documentation where possible. A detailed description of each approach, its assumptions, package options, etc. We will cover statistical methods developed to address several of these aims with a focus on introducing you to their implementation in R. Exploring the phylogenetic relatedness of a set of organisms.Assessing microbial network structures and patterns of co-occurance.Predicting a response from a set of taxonomic features.Identifying differentially abundant taxa.Estimating within- and between-sample diversity.Describing the microbial community composition of a set of samples.However, you may be surprised to find that projects on very different topics often have overarching analytic aims such as: Approaching the analysis of microbiome data with a single workflow in mind is generally not a great idea, as there is no “one size fits all” solution for the assorted set of questions one might want to answer. The diverse goals and technical variation of metagenomic research projects does not allow for a standard “analytic pipeline” for microbiome data analysis. However, other platforms such as QIIME2, biobakery and USEARCH, just to name a few, offer excellent integrated solutions for the processing and analysis of amplicon and/or shotgun metagenomic sequence data. We chose to emphasize R for this course because of the rapid development of methods and packages provided in the R language, the breadth of existing tutorials and resources, and the ever expanding community of R users. It will also serve to introduce you several popular R packages developed specifically for microbiome data analysis. The goal of this session is to provide you with a high-level 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. 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.
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