Statistical Comparison of Rank Lists and Molecular Profiles
- 技术优势
- Fast, sensitive, and quantitative determination of the degree of overlap between two genome-scale ranked gene lists. Surveys the whole range of gene-expression changes and thus avoids missing the point of maximal overlap signal, as can occur when using fixed thresholds. In a single visualization and with minimal data pre-processing, the RRHO map indicates which of several possible overlap trends is present in expression signature comparisons and the strength of the observed trend.
- 技术应用
- Individuals interested in comparing genome-scale molecular data sets.
- 详细技术说明
- Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We have developed a threshold-free algorithm called Rank-rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank-rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles.
- *Abstract
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The RRHO algorithm allows for the comparison of two gene expression signatures. Each signature is processed as a ranked list based on expression differences between two classes of samples. The signatures can be input either as raw expression data and sample and class labels, or as a pre-ranked gene list.
- *Applications
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- Individuals interested in comparing genome-scale molecular data sets.
- *Principal Investigation
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Name: Richard Taschereau
Department:
Name: Thomas Graeber
Department:
Name: Seema Plaisier
Department:
Name: Justin Wong
Department:
- 其他
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State Of Development
This software may be licensed online for non-for-profit, academic research use at no charge. Details on how to request a license online are available here.
For commercial use inquiries, please contact: innovation@research.ucla.edu.
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Tech ID/UC Case
23010/2011-035-0
Related Cases
2011-035-0
License online
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- 美国
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