Cosbin: cosine score-based iterative normalization of biologically diverse samples.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Accurate gene expression normalization is crucial for reliable data analysis across samples and conditions.
• Traditional normalization methods struggle with biologically diverse samples due to reference gene variability and asymmetric differential expression.
• Existing methods can be problematic when significant asymmetry exists in differential gene expression patterns.

Purpose of the Study:

• To introduce Cosbin (Cosine score-based iterative normalization), an efficient and accurate data-driven normalization method.
• To address the challenge of normalizing biologically diverse samples with asymmetric differential expression.
• To provide a tool that complements existing normalization techniques for improved molecular signal detection.

Main Methods:

• Cosbin utilizes Cosine scores of cross-condition expression patterns.
• The pipeline iteratively eliminates asymmetric differentially expressed genes.
• Consistently expressed genes are identified to calculate sample-wise normalization factors.

Main Results:

• Cosbin demonstrates superior performance and utility compared to six representative peer methods.
• Validation was performed using both simulated and real multi-omics expression datasets.
• The method effectively normalizes biologically diverse samples, reducing bias from asymmetric expression.

Conclusions:

• Cosbin offers an efficient and accurate solution for normalizing gene expression data, particularly in complex biological systems.
• The tool enhances the ability of biologists to detect true molecular signals amidst phenotypic diversity.
• Cosbin is implemented as open-source R scripts, freely available for use and further development.