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Gene expression analysis sits at the heart of computational biology—it's how we move from knowing what genes exist to understanding when, where, and how much they're actually doing. You're being tested on your ability to distinguish between data generation methods (how we measure expression), statistical approaches (how we find meaningful patterns), and interpretation frameworks (how we make biological sense of results). These concepts appear constantly in exam questions about experimental design, data analysis pipelines, and biological inference.
The methods in this guide form a complete analytical workflow: from generating raw expression data, to identifying significant changes, to placing those changes in biological context. Don't just memorize technique names—know what kind of question each method answers and when you'd choose one approach over another. Understanding the strengths, limitations, and appropriate applications of each method will serve you far better than rote recall.
These methods produce the raw expression measurements that feed all downstream analyses. Each technology has distinct sensitivity, throughput, and cost trade-offs that determine when it's the right choice.
Compare: RNA-Seq vs. Microarray—both measure genome-wide expression, but RNA-Seq detects novel transcripts and has higher sensitivity while microarrays are limited to known sequences on the chip. If an exam asks about discovering new splice variants, RNA-Seq is always the answer.
Compare: Bulk RNA-Seq vs. scRNA-Seq—bulk averages expression across thousands of cells, while scRNA-Seq preserves cell-to-cell variation. Choose scRNA-Seq when cellular heterogeneity matters (tumors, development, immune responses).
Once you have expression data, these approaches identify which genes show meaningful differences between conditions while controlling for noise and multiple testing.
Compare: Differential expression vs. GSEA—differential expression finds individual genes with significant changes, while GSEA asks whether groups of functionally related genes show coordinated shifts. Use both: differential expression for specific targets, GSEA for pathway-level insights.
Gene expression datasets have thousands of dimensions (genes). These methods reveal structure, identify patterns, and make visualization possible.
Compare: PCA vs. Hierarchical Clustering—PCA shows overall sample relationships in reduced dimensions, while clustering explicitly groups similar items and shows the hierarchy of relationships. PCA is better for outlier detection; clustering is better for identifying discrete groups.
These methods place expression changes in biological context, connecting statistical findings to mechanisms and functions.
Compare: Pathway Analysis vs. Co-expression Networks—pathway analysis uses prior knowledge from curated databases, while co-expression networks are data-driven and can reveal novel functional relationships. Pathway analysis is more interpretable; networks can discover unexpected connections.
| Concept | Best Examples |
|---|---|
| Transcriptome-wide measurement | RNA-Seq, Microarray, scRNA-Seq |
| Targeted validation | qPCR |
| Single-cell resolution | scRNA-Seq |
| Finding significant genes | Differential Gene Expression Analysis |
| Pathway-level significance | GSEA |
| Dimensionality reduction | PCA |
| Grouping by similarity | Hierarchical Clustering |
| Functional interpretation | Pathway Analysis, GSEA |
| Data-driven network discovery | Co-expression Network Analysis |
| Quality control and outlier detection | PCA |
You've identified 500 differentially expressed genes but want to know which biological processes are affected. Which two methods would you use, and how do they differ in their approach?
A researcher wants to study how different immune cell types respond to infection. Why would scRNA-Seq be preferred over bulk RNA-Seq, and what computational challenges would this choice introduce?
Compare RNA-Seq and microarrays: under what circumstances might microarrays still be the better choice despite RNA-Seq's higher sensitivity?
You run PCA on your RNA-Seq samples and find that PC1 separates samples by processing batch rather than experimental condition. What does this indicate, and what should you do before differential expression analysis?
Explain why GSEA might detect a significantly affected pathway even when no individual gene in that pathway passes the differential expression significance threshold.