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Differential single-cell RNA-seq analysis/Evidence
Method evidence record

Differential single-cell RNA-seq analysis

Differential single-cell RNA-seq (scRNA-seq) analysis is a computational pipeline that compares transcriptomic profiles across biological conditions — such as treated versus untreated, disease versus healthy, or time points — at single-cell resolution. It identifies which genes, cell types, and cell states change between conditions, providing mechanistic insight that bulk RNA-seq comparisons cannot offer. The approach combines clustering, cell-type annotation, and statistical testing, typically using pseudobulk aggregation to account for within-sample correlation.

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Differential Single-Cell RNA Sequencing Analysis
Taxonomic method record · process-pipeline / bioinformatics
  • Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20, 296. · URL
  • Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Lefoulon, E., Tani, N., Bhatt, D. L., Rossetti, A., & Courtine, G. (2021). Confronting false discoveries in single-cell differential expression. Nature Communications, 12, 5692. · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketRNA-seq Differential Expressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSingle-cell Gene Set Enrichment Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSingle-cell RNA-seq analysismachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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