Introduction to microbiome data science
1
Overview
1.1
Introduction
1.2
Learning goals
1.3
Acknowledgments
2
Program
2.1
Day 1: from raw sequences to ecological data analysis
2.2
Day 2 - Alpha diversity
2.3
Day 3 - Beta diversity
2.4
Day 4- Differential abundance
2.5
Day 5 : Presentations & closing
3
Getting started
3.1
Checklist (before the course)
3.2
Support and resources
3.3
Installing and loading the required R packages
4
Reproducible reporting with Rmarkdown
5
Importing microbiome data
5.1
Data access
5.2
Importing microbiome data in R
5.3
Example solutions
6
Microbiome data exploration
6.1
Data structure
6.1.1
Transformations
6.1.2
Aggregation
6.2
Visualization
6.3
Exercises (optional)
7
Alpha diversity
7.1
Visualization
7.2
Statistical testing and comparisons
7.3
Exercises
8
Beta diversity
8.1
Examples of PCoA with different settings
8.1.1
PCoA for ASV-level data with Bray-Curtis
8.1.2
PCoA for ASV-level data with Aitchison distance
8.1.3
PCoA aggregated to Phylum level
8.2
Highlighting external variables
8.2.1
Discrete grouping variable shown with colors
8.2.2
PCoA plot with continuous variable
8.3
Estimating associations with an external variable
8.4
Community typing
8.5
Exercises
9
Differential abundance analysis
9.1
Wilcoxon test
9.2
ANCOM-BC
9.3
Comparison of the methods
9.4
Comparison of abundance
10
Study material
10.1
Lecture slides
10.2
R programming resources
10.3
Resources for TreeSummarizedExperiment
10.4
Resources for phyloseq
10.5
Further reading
11
Additional Community Typing
11.1
Community composition
11.1.1
Composition barplot
11.1.2
Composition heatmap
11.2
Cluster into CSTs
11.2.1
Elbow Method
11.2.2
Silhouette Method
11.2.3
Gap-Statistic Method
11.3
Holmes and McMurdie Workflow
11.3.1
Jensen-Shannon Distance
11.3.2
NonMetric Multidimensional Scaling
11.3.3
Chi-Squared Correspondence Results
11.3.4
Bray-Curtis MDS
11.3.5
Bray-Curtis NMDS
11.3.6
Other distances from the Philentropy package
11.3.7
Add a Clustering Variable
11.3.8
More Clustering
11.4
CST Analysis
11.5
Session Info
11.6
Bibliography
12
Exercise Solutions
12.1
Section 5
12.2
Section 6
12.3
Section 7
12.4
Section 8
13
Tutorial Aims
14
Lab 1
14.1
unsupervised learning: Feature selection & dimension reduction
14.2
Load the data
15
Lab 2
15.1
unsupervised learning: clustering & visualization
16
Lab 3
16.1
unsupervised learning:Analysis and interpretation
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Questions for Lab 1, 2 and 3
Chapter 12
Exercise Solutions
This section includes exemplary solutions to the exercises presented earlier.
12.1
Section 5
Link:
Rmd
12.2
Section 6
Links:
Rmd
12.3
Section 7
Links:
Rmd
12.4
Section 8
Links:
Rmd