BlackSheep: A Bioconductor and Bioconda Package for Differential Extreme Value Analysis

Area of Science:

• Bioinformatics
• Cancer Genomics
• Computational Biology

Background:

• High-resolution molecular tumor characterization using assays like shotgun proteomics and RNA-seq is crucial.
• Interpreting highly variable molecular data and identifying biological insights from extreme measurements presents challenges.
• Rare cancer subtypes are often underrepresented, complicating analysis within larger cohorts.

Purpose of the Study:

• To develop a strategy for identifying molecules aberrantly enriched in small sample cohorts.
• To present BlackSheep, a package for nonparametric description and differential analysis of genome-wide data.
• To offer a complementary tool for differential expression analysis, particularly for small subgroups.

Main Methods:

• Developed the BlackSheep package for nonparametric description and differential analysis.
• Utilized genome-wide data, including shotgun proteomics and RNA-seq.
• Focused on analyzing small sample cohorts and rare disease subtypes.

Main Results:

• BlackSheep provides a strategy for identifying molecules enriched in small cohorts.
• The package facilitates the analysis of genome-wide data with highly varied distributions.
• It is particularly useful for analyzing small subgroups within larger cancer cohorts.

Conclusions:

• BlackSheep is a valuable tool for cancer research, especially for rare diseases.
• The package enhances the ability to find biological insights from underrepresented sample groups.
• BlackSheep complements existing differential expression analysis methods for complex genomic data.