• 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?
    • 1.1 Explanation
    • 1.2 Shell Code
    • 1.3 R Note
  • 2 How do you obtain example microbiome sequencing data for analysis?
    • 2.1 Explanation
    • 2.2 Shell Code
    • 2.3 Python Note
    • 2.4 R Note
  • 3 How do you process raw sequencing data into a feature table using QIIME2?
    • 3.1 Explanation
    • 3.2 Shell Code (QIIME2 CLI)
    • 3.3 Python Note
  • 4 How do you process raw sequencing data into a feature table using Mothur?
    • 4.1 Explanation
    • 4.2 Shell Code
    • 4.3 R Note
    • 4.4 Python Note
  • 5 How do you explore and summarize a microbiome OTU table?
    • 5.1 Explanation
    • 5.2 Python Code
    • 5.3 R Code
  • 6 How do you filter out low-abundance or low-prevalence OTUs?
    • 6.1 Explanation
    • 6.2 Python Code
    • 6.3 R Code
  • II DATA VISUALIZATION
  • 7 How do you visualize total OTU abundance per sample?
    • 7.1 Explanation
    • 7.2 Python Code
    • 7.3 R Code
  • 8 How do you create a stacked bar plot of top genera across samples?
    • 8.1 Explanation
    • 8.2 Python Code
    • 8.3 R Code
  • 9 How do you visualize alpha diversity (richness) across groups?
    • 9.1 Explanation
    • 9.2 Python Code
    • 9.3 R Code
  • 10 How do you perform ordination (e.g., PCA) to visualize sample clustering?
    • 10.1 Explanation
    • 10.2 Python Code
    • 10.3 R Code
  • 11 How do you visualize OTU or Genus abundance using a heatmap?
    • 11.1 Explanation
    • 11.2 Python Code
    • 11.3 R Code
  • III STATISTICAL ANALYSIS
  • 12 How do you statistically compare OTU richness between groups?
    • 12.1 Explanation
    • 12.2 Python Code
    • 12.3 R Code
  • 13 How do you test for correlation between alpha diversity and age?
    • 13.1 Explanation
    • 13.2 Python Code
    • 13.3 R Code
  • 14 How do you compare alpha diversity across 3 or more groups?
    • 14.1 Explanation
    • 14.2 Python Code
    • 14.3 R Code
  • 15 How do you test for differences in community composition using PERMANOVA?
    • 15.1 Explanation
    • 15.2 Python Code
    • 15.3 R Code
  • 16 How do you test for differential abundance of OTUs across groups?
    • 16.1 Explanation
    • 16.2 Python Code
    • 16.3 R Code
  • IV MACHINE LEARNING
  • 17 How do you prepare microbiome data for machine learning?
    • 17.1 Explanation
    • 17.2 Python Code
    • 17.3 R Note
  • 18 How do you train and evaluate a Random Forest classifier on microbiome data?
    • 18.1 Explanation
    • 18.2 Python Code
    • 18.3 R Code (caret)
  • 19 How do you build a Logistic Regression model for microbiome classification?
    • 19.1 Explanation
    • 19.2 Python Code
    • 19.3 R Code (caret)
  • 20 How do you train a Support Vector Machine (SVM) for microbiome classification?
    • 20.1 Explanation
    • 20.2 Python Code
    • 20.3 R Code (caret)
  • 21 How do you apply Gradient Boosting (XGBoost) for microbiome classification?
    • 21.1 Explanation
    • 21.2 Python Code
    • 21.3 R Code (caret + xgboost)
  • 22 How do you visualize ROC curves to compare classification models?
    • 22.1 Explanation
    • 22.2 Python Code
    • 22.3 R Code (caret + pROC)
  • 23 How do you apply cross-validation strategies to evaluate model reliability?
    • 23.1 Explanation
    • 23.2 Python Code
    • 23.3 R Code (caret with repeated k-fold CV)
  • 24 How do you use mikropml in R for microbiome machine learning?
    • 24.1 Explanation
    • 24.2 R Code
    • 24.3 Notes
  • APPENDIX
  • A Microbiome Data Analysis Workflow
  • Explore More Guides

Unlocking Microbial Insights

Unlocking Microbial Insights


Last updated: June 22, 2025