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Updated: Mar 5, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

RefBool: a reference-based algorithm for discretizing gene expression data.

Sascha Jung¹, Andras Hartmann¹, Antonio Del Sol¹

¹Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.

Bioinformatics (Oxford, England)

|March 24, 2017

Summary

This summary is machine-generated.

RefBool discretizes gene expression data, introducing a third intermediate state and statistical significance values. This novel reference-based algorithm improves upon existing methods for gene regulatory analysis.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Gene expression data analysis often requires discretization into active/inactive states.
Current methods may lead to inaccurate conclusions due to lack of statistical support.

Purpose of the Study:

Introduce RefBool, a reference-based algorithm for improved gene expression data discretization.
Enhance accuracy by allowing an intermediate state and providing statistical significance values.

Main Methods:

Developed RefBool, a novel algorithm for gene expression data discretization.
Incorporated p- and q-values to indicate classification significance.
Validated on a neuroepithelial differentiation study.

Main Results:

RefBool allows classification of an intermediate gene expression state.
Associated p- and q-values provide statistical confidence for each classification.
Qualitative and quantitative comparisons show RefBool's superiority over 10 existing methods.

Conclusions:

RefBool offers a more robust approach to gene expression data discretization.
The algorithm enhances the reliability of downstream analyses in systems biology.
Provides improved clusterings compared to current methodologies.