Multi-factor Model: ~condition + batch
Model ConfigurationAdd additional factors or continuous covariates to the design formula so that confounding variables are controlled while testing your primary factor of interest.
When to Use
- Your samples were processed in different batches, on different sequencing lanes, or at different sites and you need to account for this technical variation.
- You want to adjust for a continuous covariate such as RNA integrity number (RIN), patient age, or library size.
- PCA of your count data shows separation driven by batch rather than by your experimental factor.
Required Inputs
- The primary factor of interest plus one or more additional effects or covariates selected from metadata columns.
- The design preview (e.g., ~batch + condition) updates dynamically from your selections.
- Categorical covariates must have two or more levels; continuous covariates must contain numeric values.
What to Expect
- easyCris adjusts for all configured covariates before estimating the fold change for your primary contrast.
- Results reflect the condition effect after accounting for batch or other confounders, which often increases the number of significant genes.
- Adding a meaningful batch term typically increases statistical power by absorbing unexplained variance.
Common Pitfalls
- Confounded designs -- where all treated samples are in Batch 1 and all controls are in Batch 2 -- cannot separate the treatment effect from the batch effect. The model cannot resolve this.
- Including too many covariates relative to sample size causes overfitting. Keep the model parsimonious: include only covariates with biological or technical justification.
- Covariates with very low variation across samples add noise without improving the model and can make fitting unstable.
Citations
References
- Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550.
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289-300.