Microbial Insights
Welcome to CDI – Unlocking Microbial Insights
- 📚 The CDI Learning Path
I DATA EXPLORATION
1 What are the essential tools for microbiome read quality control?
2 How do you obtain example microbiome sequencing data for analysis?
3 How do you process raw sequencing data into a feature table using QIIME2?
4 How do you process raw sequencing data into a feature table using Mothur?
5 How do you explore and summarize a microbiome OTU table?
6 How do you filter out low-abundance or low-prevalence OTUs?
II DATA VISUALIZATION
7 How do you visualize total OTU abundance per sample?
8 How do you create a stacked bar plot of top genera across samples?
9 How do you visualize alpha diversity (richness) across groups?
10 How do you perform ordination (e.g., PCA) to visualize sample clustering?
11 How do you visualize OTU or Genus abundance using a heatmap?
III STATISTICAL ANALYSIS
12 How do you statistically compare OTU richness between groups?
13 How do you test for correlation between alpha diversity and age?
14 How do you compare alpha diversity across 3 or more groups?
15 How do you test for differences in community composition using PERMANOVA?
16 How do you test for differential abundance of OTUs across groups?
IV MACHINE LEARNING
17 How do you prepare microbiome data for machine learning?
18 How do you train and evaluate a Random Forest classifier on microbiome data?
19 How do you build a Logistic Regression model for microbiome classification?
20 How do you train a Support Vector Machine (SVM) for microbiome classification?
21 How do you apply Gradient Boosting (XGBoost) for microbiome classification?
22 How do you visualize ROC curves to compare classification models?
23 How do you apply cross-validation strategies to evaluate model reliability?
24 How do you use mikropml in R for microbiome machine learning?
APPENDIX
A Microbiome Data Analysis Workflow
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Unlocking Microbial Insights

A Microbiome Data Analysis Workflow