Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study...

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Computational Biology

Background:

• Gene expression profiling using RNA sequencing (RNA-seq) is vital for understanding disease mechanisms and drug development.
• High costs of RNA-seq limit its widespread application.
• Sample multiplexing reduces costs but lowers sequencing depth, potentially affecting downstream analyses.

Purpose of the Study:

• To investigate the impact of altered sequencing depth on RNA-seq-based gene expression connectivity mapping.
• To assess the robustness of target compound identification for glioma treatment under simulated data loss.

Main Methods:

• Assembled published RNA-seq profiles from glioma patients into gene signature contrasts.
• Simulated sequencing depth alterations by subsampling gene signatures.
• Analyzed subsampled signatures using connectivity mapping to evaluate target compound identification.

Main Results:

• Data loss resulted in both loss and gain of significant gene expression connections.
• More accurate gene signatures demonstrated greater resilience to data loss, identifying robust target compounds.
• Lost connections were associated with low-abundance genes, while consistently identified connections involved highly expressed genes.
• False-positive connections were introduced due to gene signature modification from data loss.

Conclusions:

• Gene signature accuracy is essential for reliable connectivity mapping.
• Improving gene signature quality can enhance the clinical utility of discovering new therapeutic compounds.
• This study underscores the importance of data integrity in RNA-seq analyses for drug repurposing.