Sigscores: summary scores for molecular signatures in R

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Multi-omics data expansion necessitates robust methods for biomarker discovery.
• Existing molecular signatures often lack reproducibility and broad applicability.
• Summarizing complex molecular profiles into reliable scores remains a challenge.

Purpose of the Study:

• Introduce sigscores, an R package for streamlined computation of molecular signature summary scores.
• Enhance the reproducibility and applicability of molecular signatures.
• Provide tools for rigorous statistical assessment and diagnostic evaluation of signatures.

Main Methods:

• Developed sigscores, an R package building on sigQC principles.
• Implemented diverse scoring metrics (central tendency, dispersion, aggregation).
• Integrated a resampling framework for empirical null distributions and significance testing.
• Included visualization tools for diagnostic evaluation.

Main Results:

• Sigscores streamlines the computation of statistically sound and biologically meaningful scores for molecular signatures.
• The package supports a wide range of scoring metrics and rigorous significance assessment.
• Optimized for parallel processing, enabling high-throughput and large-scale applications.

Conclusions:

• Sigscores offers a robust solution for summarizing molecular signatures from multi-omics data.
• The R package facilitates improved biomarker development for diagnosis, prognosis, and therapeutic decisions.
• Sigscores is suitable for both exploratory research and large-scale bioinformatics applications.