Differential Expression
Differential Expression - Related Videos
03:08
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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2025
The current work describes a protocol for running the Pathway2Targets algorithm, an R script that predicts and prioritizes therapeutic targets based on the profile of intracellular signaling pathways generated by comparing case versus control samples from a bulk RNA-sequencing experiment.
10:10
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Cited by 309 •
2021
A detailed protocol of differential expression analysis methods for RNA sequencing was provided: limma, EdgeR, DESeq2.
06:47
3D Organotypic Co-culture Model Supporting Medullary Thymic Epithelial Cell Proliferation, Differentiation and Promiscuous Gene Expression
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Cited by 15 •
2015
Studying medullary thymic epithelial cells in vitro has been largely unsuccessful, as current 2D culture systems do not mimic the in vivo scenario. The 3D culture system described herein - a modified skin organotypic culture model - has proven superior in recapitulating mTEC proliferation, differentiation and maintenance of promiscuous gene expression.
08:29
Expression Profiling with Microarrays
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2023
Microarrays are important tools for profiling gene expression, and are based on complementary binding between probes that are attached to glass chips and nucleic acids derived from samples. Using these arrays, scientists can simultaneously evaluate the expression of thousands of genes. In addition, the expression profiles of different cells or tissue types can be compared, allowing researchers to deduce how the expression of different genes change during biological processes, and thus gain...
10:34
Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Cited by 2 •
2010
This procedure describes a quick and easy workflow to introduce siRNA into difficult to transfect cell lines and follow gene expression by real-time PCR. Use of an automated cell counter, multi-well electroporation plate, and automated electrophoresis station provide quick and reliable results without the need for expensive robotic handling.
FAQs
Normalization adjusts RNA abundance measurements so samples can be compared on a consistent basis. Without this step, differences in sequencing or microarray measurements could obscure or exaggerate biological changes. Properly normalized data provide a more reliable foundation for estimating fold changes, testing whether observed differences are statistically meaningful, and identifying molecular changes associated with a disease or other phenotype.
Fold change describes the magnitude and direction of a difference in RNA abundance between conditions, whereas statistical testing evaluates whether that difference is likely to reflect a real biological change. Because many genes or transcripts may be tested simultaneously, controlling the false discovery rate limits the expected proportion of incorrectly identified findings among the results considered significant.
RNA sequencing and microarrays provide alternative ways to measure RNA abundance across biological samples. Those measurements form the input for normalization, fold-change estimation, and statistical comparison between conditions. The resulting analysis can highlight transcripts whose activity differs between healthy and diseased tissues, helping connect molecular patterns with phenotypes studied in medicine.
A typical workflow compares RNA measurements between defined biological conditions, normalizes the data, estimates fold changes, and applies statistical tests with false discovery rate control. The resulting list of changed genes or transcripts can then support pathway analysis, which examines whether the findings cluster into biological processes relevant to disease mechanisms, treatment response, or a clinical phenotype.
Genes or transcripts showing condition-associated changes may provide candidates for biomarkers. In medicine, such candidates can help distinguish disease-related molecular states or provide information related to likely outcomes, supporting diagnostic and prognostic research. Their value comes from linking reproducible expression patterns with clinically relevant phenotypes rather than treating every statistically detected change as an immediately usable biomarker.
Comparing tumor or other disease samples with appropriate biological conditions can reveal molecular changes associated with tumor behavior or response to treatment. These findings may clarify disease mechanisms, identify pathways affected by therapy, and suggest targets for further investigation. In precision medicine, expression patterns can help support strategies that relate molecular characteristics to treatment selection.