Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Gene co-expression analysis identifies coordinated gene expression patterns.
• RNA-Sequencing data normalization is crucial for accurate analysis.
• Certain normalization methods, like quantile-based ones, can introduce false associations.

Purpose of the Study:

• To develop a normalization method that avoids false-positive associations in gene co-expression analysis.
• To improve the accuracy of co-expression network inference from RNA-Sequencing data.
• To address limitations of existing normalization techniques in handling large-scale heterogeneous data.

Main Methods:

• Developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a novel normalization method.
• SNAIL is based on smooth quantile normalization, specifically designed for co-expression inference.
• Evaluated SNAIL's performance in avoiding false-positive associations in co-expression and network analyses.

Main Results:

• SNAIL effectively prevents the formation of false-positive associations in co-expression measurements.
• The method supports downstream network analyses without introducing spurious links.
• SNAIL allows for the retention of associations involving genes with low expression in specific sample subgroups, avoiding arbitrary filtering.

Conclusions:

• SNAIL is a robust normalization method for gene co-expression analysis using RNA-Sequencing data.
• The method enhances the reliability of co-expression network construction.
• SNAIL has significant potential for advancing network modeling and association-based approaches in large-scale biological data analysis.