Why Do Significant Gene Lists Change After Adding Additional Conditions in Differential Gene Expression Analysis?

DESeq2 is a popular method for differential expression analysis of count data from RNA-seq experiments. It estimates fold changes and tests for differential expression using a negative binomial generalized linear model. When you perform a differential expression analysis using DESeq2, the results may vary depending on the experimental conditions included in the analysis. In your case, the significant gene list differs between the two analyses: one with condition A and B, and the other with condition A, B, and C.

There are several reasons for this discrepancy:

1. Normalization factors: DESeq2 estimates size factors for normalization of read counts across samples. When you add an additional condition (condition C in your case), the normalization factors may change, which can affect the fold change estimates and ultimately the list of differentially expressed genes.

2. Dispersion estimation: DESeq2 uses a shrinkage estimator for dispersions, which are gene-specific measures of biological variability across replicates. Including an additional condition may affect the dispersion estimates, and therefore influence the list of differentially expressed genes.

3. Multiple testing correction: DESeq2 uses the Benjamini-Hochberg procedure to control the false discovery rate (FDR) when testing for differentially expressed genes. With the addition of another condition, the number of tests may change, which can affect the FDR threshold and consequently the list of significant genes.

4. Model fitting: DESeq2 fits a negative binomial generalized linear model to the count data. Including an additional condition may affect the model fitting, leading to different estimates of the coefficients and p-values, which in turn can affect the list of differentially expressed genes.

These factors can contribute to the differences in the significant gene list between the two analyses. To minimize discrepancies, ensure that you have a well-designed experiment with biological replicates for each condition and carefully consider the comparisons of interest when setting up the design matrix for DESeq2 analysis.

The choice between inputting A and B or inputting A, B, and C depends on your research goals and the specific comparisons you want to make. Both methods can be appropriate, but they address different questions.

• Input A and B: If you are interested in comparing the gene expression profiles between conditions A and B, and condition C is not relevant to this specific comparison, then inputting only A and B would be a better choice. By analyzing only the conditions relevant to your research question, you can focus on the specific contrasts of interest, making the interpretation of the results more straightforward.

• Input A, B, and C: If your research goals involve understanding the gene expression profiles in a broader context, such as comparing all three conditions or investigating how the expression profiles change across a time course or gradient, then inputting A, B, and C would be more appropriate. In this case, including all conditions in the analysis will provide a more comprehensive view of the gene expression changes, and the comparisons between the different conditions can help identify genes that show unique expression patterns or that are specific to certain conditions.

In summary, the choice between inputting A and B or inputting A, B, and C depends on your research question and objectives. It is crucial to clearly define the comparisons you are interested in and to consider the biological context of your study before deciding which method to use.

When adding an additional condition to a differential gene expression analysis, it's natural for the results to change due to factors such as normalization, dispersion estimation, model fitting, and multiple testing correction, as previously discussed. However, there are some strategies you can apply to minimize the impact of adding an additional condition and make your results more robust:

1. Independent analysis: Analyze the pairwise comparisons separately (A vs B, A vs C, and B vs C), and then compare the lists of significant genes obtained from each analysis. This approach keeps the comparisons of interest independent of the other conditions.

2. Batch effect correction: If the additional condition introduces batch effects, you can use methods like ComBat from the sva package or limma's removeBatchEffect function to correct for these batch effects before running differential expression analysis. This can help reduce the impact of adding an additional condition on the results.

3. Consistent normalization methods: Use consistent normalization methods across all samples, even if you are adding a new condition. This will ensure that the read counts are comparable across all samples, reducing the impact of the additional condition on the results.

4. Incorporate the additional condition in the model: If you want to include the additional condition in your analysis but keep the results between A and B consistent, you can include the additional condition as a covariate in your model. This will account for the effect of the additional condition while still allowing you to make the comparisons of interest.

5. Intersection of significant genes: Perform the differential expression analysis with and without the additional condition, and then take the intersection of the significant genes from both analyses. The intersecting set of genes is likely to be more robust against the addition of the extra condition.

However, it's important to note that adding an additional condition will inherently influence the results to some extent, as the analysis is dependent on the data provided. The strategies mentioned above can help to minimize the impact of adding an additional condition, but they cannot completely eliminate it.

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