Shambhala: a platform-agnostic data harmonizer for gene expression data.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Gene expression data harmonization is crucial for comparative analysis.
• Existing methods often limit harmonization to two datasets.
• Diverse experimental methods and platforms generate incompatible gene expression profiles.

Purpose of the Study:

• Introduce Shambhala, a novel bioinformatics tool for harmonizing multiple human gene expression datasets.
• Enable cross-platform and cross-experimental method compatibility for gene expression data.
• Facilitate robust comparative analyses of large-scale gene expression datasets.

Main Methods:

• Shambhala calibrates gene expression profiles using an auxiliary standardization dataset.
• Profiles are transformed to emulate the output of the Affymetrix Human Gene platform.
• The tool was evaluated for its ability to retain biological features post-harmonization.

Main Results:

• Shambhala successfully harmonizes multiple gene expression datasets into a universal format.
• Unlike other methods, Shambhala preserves biologically meaningful clustering across different platforms.
• Hierarchical clustering analysis confirmed Shambhala's effectiveness on multi-platform data.

Conclusions:

• Shambhala provides sample-specific and platform-independent biologically meaningful clustering.
• It outperforms quantile normalization and DESeq/DESeq2 normalization for multi-platform data.
• Shambhala is a valuable tool for comparative genomics research using diverse datasets.