cdev: a ground-truth based measure to evaluate RNA-seq normalization performance.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-seq) data normalization is essential for accurate gene expression analysis.
• Existing performance measures for RNA-seq normalizers are often qualitative, biased, or confounded by downstream analysis parameters.
• There is a need for objective and reliable methods to evaluate RNA-seq normalization techniques.

Purpose of the Study:

• To introduce a novel, quantitative metric for assessing RNA-seq normalization performance.
• To develop a robust benchmark dataset for evaluating normalization methods using external spike-ins.
• To provide a standardized toolset for benchmarking RNA-seq normalizers.

Main Methods:

• Proposed a new metric, condition-number based deviation (cdev), to quantify the difference between expression matrices.
• Compiled an extensive dataset of public RNA-seq experiments with external spike-ins to establish experimental ground truth.
• Utilized the cdev metric and the benchmark dataset to evaluate the performance of various normalization methods.

Main Results:

• The cdev metric provides a quantitative measure of normalization success.
• The benchmark dataset enables objective comparison of different RNA-seq normalization algorithms.
• The proposed toolset facilitates the identification of superior normalization strategies.

Conclusions:

• The cdev metric and the curated dataset offer a valuable resource for the bioinformatics community.
• These tools address the limitations of existing methods for evaluating RNA-seq normalization.
• This work will aid in the development and selection of more effective RNA-seq data processing pipelines.