Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.

Area of Science:

• Bioinformatics
• Genomics
• Data Science

Background:

• Next-generation sequencing (NGS) generates high-throughput data from numerous biological samples.
• Experimental batch processing is common but often not reported, impacting sample quality and statistical analysis.
• Existing bioinformatics methods may misinterpret batch effects as biological signals, necessitating quality-aware approaches.

Purpose of the Study:

• To develop and validate a machine learning tool for automated quality assessment of NGS samples.
• To detect and correct batch effects in RNA-seq datasets using sample quality scores.
• To evaluate the performance of the quality-aware correction method against established techniques.

Main Methods:

• Developed statistical guidelines and a machine learning tool for NGS sample quality evaluation.
• Applied the quality assessment tool to 12 public RNA-seq datasets with known batch information.
• Utilized the quality score to distinguish batches and correct for batch effects in sample clustering.
• Evaluated correction efficacy with and without outlier removal, comparing to a reference method.

Main Results:

• The quality score successfully distinguished batches across 12 RNA-seq datasets.
• Batch effect correction using the quality score was comparable or superior to the reference method in 92% of datasets.
• Coupling quality-aware correction with outlier removal further improved performance, outperforming the reference method in 92% of datasets.

Conclusions:

• The developed software effectively detects batches in RNA-seq data based on sample quality predictions.
• The study demonstrates the utility of sample quality insights for correcting batch effects.
• While batch effects correlate with quality differences, other artifacts exist, emphasizing the need for robust statistical correction in experimental design.