How to fix common DESeq2 or edgeR errors?

Common Errors in DESeq2 and edgeR

• Verify the installation of packages. Ensure that DESeq2, edgeR, and their dependencies are correctly installed. You can update or reinstall them using Bioconductor.

 

• Check for version compatibility. Make sure that the R version and the package versions are compatible. Update R and Bioconductor packages if necessary.

Data Import Errors

• Ensure that count data is correctly formatted, with genes as rows and samples as columns. The matrix should contain only non-negative integers.

 

• Inspect for missing values, as these should not be present in count data. Handle any NAs either by imputing values or removing the affected genes or samples.

Design Matrix Issues

• Check that the design matrix is correctly specified, matching the samples in your count matrix. It should include all necessary experimental factors.

 

• Avoid using columns in the design matrix that have zero variance, as these can cause singularities in model fitting.

Model Fitting Errors

• When encountering convergence errors, consider simplifying your model. Ensure that your design formula is appropriate for the complexity of your experiment.

 

• Verify the dispersion estimates, especially for edgeR. Explore using more robust estimation options if the defaults do not perform well.

Normalization Problems

• Check if library size factors or normalization methods are properly calculated. Both DESeq2 and edgeR provide specific functions to handle this which should be executed before differential expression analysis.

 

• If certain samples are outliers, consider whether the normalization factors might be skewed. You may need to exclude or specifically handle these samples.

Interpretation and Visualization

• Ensure that you are interpreting the results correctly. Double-check the contrast setting in your analysis, as incorrect specification may lead to misleading results.

 

• Visualize your data at multiple steps, including PCA plots and MA plots, to confirm that the analysis aligns with expected biological and experimental conditions.

General Debugging Tips

• Utilize detailed logging of your analysis. Redirect the output of R scripts to a log file so you can track errors effectively.

 

• Break down large analyses into smaller chunks to isolate problematic steps. This modular approach helps in identifying specific errors rather than facing cascading failures.