Introduction to microbiome data science
Chapter 1 Overview
Welcome to Radboud Summer School, July 2021
Figure source: Moreno-Indias et al. (2021) Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology 12:11.
The miaverse consists of an efficient data structure, an associated package ecosystem, demonstration data sets, and open documentation. These are explained in more detail in the online book Orchestrating Microbiome Analysis.
The training material walks you through an example workflow that shows the standard steps of taxonomic data analysis covering data access, exploration, analysis, visualization and reporoducible reporting. You can run the workflow by simply copy-pasting the examples. For advanced material, you can test and modify further examples from the OMA book, or try to apply the techniques to your own data.
1.2 Learning goals
This course provides an overview of the standard bioinformatics workflow in taxonomic profiling studies, ranging from data preprocessing to statistical analysis and reproducible reporting, with a focus on examples from human gut microbiota studies. You will become familiar with standard bioinformatics concepts and methods in taxonomic profiling studies of the human microbiome. This includes better understanding of the specific statistical challenges, practical hands-on experience with the commonly used methods, and reproducible research with R.
After the course you will know how to approach new tasks in microbiome data science by utilizing available documentation and R tools.
Target audience Advanced students and applied researchers who wish to develop their skills in microbial community analysis.
Citation “Introduction to microbiome data science (2021). URL: https://microbiome.github.io.”
We thank Felix Ernst, Sudarshan Shetty, and other miaverse developers who have contributed open resources that supported the development of the training material.
Contact Leo Lahti, University of Turku, Finland
License All material is released under the open CC BY-NC-SA 3.0 License.
The source code of this repository is fully reproducible and contains the Rmd files with executable code. All files can be rendered at one go by running the file main.R. You can check the file for details on how to clone the repository and convert it into a gitbook, although this is not necessary for the training.