CuBlock

CuBlock normalizes gene expression microarray data across multiple platforms to reduce inter-platform batch effects while preserving separation of biological groups.

Key Features:

• Cross-Platform Normalization: Normalizes gene-expression microarray data originating from different microarray platforms to mitigate inter-platform batch effects.
• Scalability and Reusability: Enables normalization without requiring all samples across all platforms to be processed simultaneously, supporting large-scale analyses.
• Performance Focus: Optimized to maximize separation of biological groups in mixed-platform datasets, demonstrated across up to six different platforms.

Scientific Applications:

• Systematic Meta-analysis: Combining extensive microarray datasets from public repositories to extract biological insights.
• Integration with Clinical Evidence: Complementing Real-World Data with findings from Randomized Controlled Trials using microarray-derived evidence.

Methodology:

Validated on two datasets (a test/standardization dataset and an experimental study dataset) and benchmarked against ComBat, YuGene, DBNorm, Shambhala, and a log2 transformation by assessing separation of biological groups in mixed-platform data (up to six platforms).