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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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GSVA: gene set variation analysis for microarray and RNA-seq data.

Sonja Hänzelmann1, Robert Castelo, Justin Guinney

  • 1Research Program on Biomedical Informatics, Hospital del Mar Medical Research Institute, Barcelona, Catalonia, Spain.

BMC Bioinformatics
|January 18, 2013
PubMed
Summary
This summary is machine-generated.

Gene Set Variation Analysis (GSVA) enhances gene set enrichment analysis by estimating pathway activity variation in unsupervised, heterogeneous datasets. This method improves detection of subtle pathway changes and is applicable to both microarray and RNA-seq data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene set enrichment (GSE) analysis condenses gene expression data into pathway summaries, offering noise reduction and biological interpretability over single-gene approaches.
  • Existing GSE methods face challenges in modeling pathway activity within highly heterogeneous datasets common in advanced molecular profiling.
  • There is a need for robust and flexible GSE methodologies to analyze complex biological data beyond simple case-control studies.

Purpose of the Study:

  • Introduce Gene Set Variation Analysis (GSVA), a novel GSE method.
  • Develop a flexible approach to estimate pathway activity variation in an unsupervised manner across sample populations.
  • Address the need for GSE methods applicable to heterogeneous data and RNA-seq experiments.

Main Methods:

  • Developed Gene Set Variation Analysis (GSVA), an unsupervised method for estimating pathway activity variation.
  • Compared GSVA's robustness against existing state-of-the-art sample-wise enrichment methods.
  • Demonstrated GSVA's utility in differential pathway activity and survival analyses.
  • Validated GSVA's applicability to both microarray and RNA-seq data.

Main Results:

  • GSVA effectively estimates pathway activity variation in an unsupervised manner.
  • GSVA shows robustness comparable to current leading sample-wise enrichment methods.
  • The method is effective for differential pathway activity and survival analyses.
  • GSVA performs analogously on both microarray and RNA-seq experimental data.

Conclusions:

  • GSVA offers increased statistical power for detecting subtle pathway activity changes compared to existing methods.
  • GSVA serves as a foundational tool for building pathway-centric biological models, extending beyond traditional endpoint analysis.
  • GSVA addresses the growing demand for GSE methods compatible with RNA-seq data.
  • GSVA is available as an open-source R package within the Bioconductor project.