Sample interview questions: How do you handle issues related to multiple comparisons or multiplicity in microbiome or complex biological data analyses?
Sample answer:
Addressing Multiple Comparisons in Microbiome and Complex Biological Data Analyses
Strategies for Handling Multiplicity
- Adjust p-values: Methods such as Bonferroni correction, FDR (False Discovery Rate) control, or Benjamini-Hochberg procedure can reduce false positives by adjusting p-values for multiple comparisons.
- Control for false discovery rate (FDR): FDR-controlling procedures (e.g., Benjamini-Hochberg or Benjamini-Yekutieli) provide a threshold for statistical significance that balances the risk of false positives with the power to detect true effects.
- Use hierarchical testing: Divide the analysis into multiple levels and perform tests at each level using a sequential approach. Significant results from earlier levels can inform subsequent tests, reducing the number of false positives.
- Group features into correlated sets: Correlated features can be treated as a single unit, reducing the effective number of comparisons and increasing statistical power.
- Reduce dimensionality: Techniques like principal component analysis (PCA) or singular value decomposition (SVD) can reduce the number of variables analyzed, mitigating the impact of multiple comparisons.
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