Sample interview questions: How do you handle batch effects or confounding factors in multi-omics integration studies?
Sample answer:
Batch Effects and Confounding Factors in Multi-Omics Integration Studies
Technical Batch Effects:
- Identification: Use principal component analysis (PCA) or unsupervised clustering to identify unknown sources of variation.
- Removal: Apply batch correction methods such as ComBat, RUV, or SVA to adjust for systematic technical differences between samples.
Biological Confounding Factors:
- Identification: Review experimental design and metadata for potential confounding factors (e.g., age, sex, environmental exposure).
- Adjustment: Include confounding factors as covariates in statistical models or use stratification to minimize their impact.
Integrated Analysis Methods:
- Constrained Correlation Analysis (CCA): Identifies shared patterns of variation across multiple omics data while controlling for confounding factors.
- Partial Least Squares (PLS): Similar to CCA but emphasizes the prediction of a specific response variable.
- Canonical variate analysis (CVA): Projects data onto… Read full answer
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