Why does adjusted p-value threshold matter in DEG identification?

Scientific Value: Enables interrogation of therapeutic hypotheses by identifying aberrant transcriptomic signatures in cholangiocarcinoma versus normal tissues.
Operational Value: Provides statistically robust gene-level metrics (log2 fold change, adjusted p-value) for prioritizing targets with high biological relevance.
Predictive Value: Supports mechanistic de-risking through convergent evidence from multiple analytical frameworks (limma, EdgeR, DESeq2).

Setting an adjusted p-value threshold (e.g.,

How does log2 fold change filtering support target prioritization?

Filtering for absolute log2 fold change ≥ 2 focuses on genes with substantial expression changes, increasing likelihood of biological relevance and therapeutic targeting potential.

What enables cross-method comparison of DEG results?

Generating normalized count data and applying consistent filtering criteria allows comparison of limma, edgeR, and DESeq2 outputs via overlap analysis such as Venn diagrams.

Why is normalization required for limma and edgeR but not DESeq2?

limma and edgeR depend on normalized input for accurate modeling, whereas DESeq2 internally estimates normalization factors from raw counts as part of its statistical framework.

How does Venn diagram analysis support target validation confidence?

Identifying overlapping differentially expressed genes across multiple methods increases confidence in true biological signals and reduces risk of method-specific false positives.