Mitigating the adverse impact of batch effects in sample pattern detection.

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Statistical Genetics

Background:

• Batch effects are common in high-throughput profiling data, including RNA-sequencing (RNA-seq).
• Existing methods often struggle to fully remove batch effects, leading to artifacts in downstream analyses.
• Residual batch effects can obscure true biological patterns and affect sample comparisons.

Purpose of the Study:

• To develop an adaptive method for directly adjusting dissimilarity matrices to correct for batch effects.
• To improve the accuracy of pattern detection methods, such as clustering and dimension reduction, in the presence of batch effects.
• To provide a robust alternative to existing batch effect correction techniques.

Main Methods:

• An adaptive method was designed to directly modify the dissimilarity matrix between samples.
• The proposed method was evaluated using simulation studies and real-world biological data.
• Performance was compared against the ComBat method, a leading batch effect adjustment technique.

Main Results:

• The adaptive method demonstrated superior performance in recovering true underlying clusters in simulation studies.
• The method effectively corrected distance matrices derived from real biological data.
• Application of the corrected distance matrices significantly improved the performance of clustering algorithms.

Conclusions:

• The novel adaptive method offers an effective approach to mitigate batch effects in pattern detection analyses.
• Directly adjusting dissimilarity matrices provides a powerful strategy for handling batch effects without altering original data.
• The R package 'QuantNorm' is available for implementing this method, facilitating its application in biological research.