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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Differential Pathway Analysis.

Jean Fan1

  • 1Department of Chemistry and Chemical Biology, Harvard University, Boston, MA, USA. jeanfan@fas.harvard.edu.

Methods in Molecular Biology (Clifton, N.J.)
|February 14, 2019
PubMed
Summary
This summary is machine-generated.

Prior knowledge enhances gene expression analysis. Pathway and gene set overdispersion analysis (PAGODA) identifies transcriptional subpopulations in single-cell RNA sequencing data.

Keywords:
ClusteringDifferential expression analysisGene set enrichment analysisPathwaySingle cell

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene expression data analysis benefits from integrating prior biological pathway knowledge.
  • Pathway enrichment testing enhances statistical power and interpretability.
  • Single-cell RNA sequencing (scRNA-seq) generates complex transcriptional data requiring advanced analytical methods.

Purpose of the Study:

  • To demonstrate the utility of gene set or pathway enrichment testing for single-cell RNA sequencing data.
  • To introduce and apply Pathway and Gene Set Overdispersion Analysis (PAGODA) for identifying transcriptional subpopulations.
  • To characterize these subpopulations based on pathway activity.

Main Methods:

  • Application of gene set and pathway enrichment testing methodologies.
  • Development and implementation of Pathway and Gene Set Overdispersion Analysis (PAGODA).
  • Analysis of single-cell RNA sequencing datasets to identify distinct transcriptional states.

Main Results:

  • PAGODA effectively identifies and characterizes transcriptional subpopulations within scRNA-seq data.
  • Pathway enrichment analysis reveals biological differences between identified subpopulations.
  • Demonstrated practical value of integrating pathway knowledge into scRNA-seq analysis.

Conclusions:

  • Pathway and gene set enrichment approaches, particularly PAGODA, are valuable tools for scRNA-seq data analysis.
  • PAGODA facilitates the discovery and characterization of cellular heterogeneity.
  • Integrating prior pathway knowledge improves the biological insights derived from gene expression studies.