BEclear

BEclear corrects batch effects in genomic datasets to preserve true biological variation, with emphasis on DNA methylation data and applicability to any matrix of real numbers.

Key Features:

• Latent Factor Models: Employs latent factor models to identify statistically significant deviations attributable to batch effects.
• Localized Correction: Replaces only data portions that deviate significantly by reconstructing typical values from neighboring data entries to minimize alteration of unaffected values.
• Applicability to Real-Valued Matrices: Operates on DNA methylation data and any matrix containing real numbers.
• Array Platform Independence: Array platform independent, enabling use across different array-based genomic platforms.
• Performance and Validation: Validated on DNA methylation data from multiple tumor datasets in The Cancer Genome Atlas (TCGA) and benchmarked against ComBat, Surrogate Variable Analysis, RUVm, and Functional normalization, showing comparable or improved performance.

Scientific Applications:

• Batch Effect Correction: Removes technical artifacts that confound analyses of genomic datasets.
• Differential Methylation Analysis: Improves the reliability of differential methylation studies by reducing batch-induced variation.
• Epigenetic and Large-Scale Genomic Studies: Supports epigenetic investigations and analyses across large-scale genomic datasets.

Methodology:

Identifies batch-associated latent factors and applies localized correction by reconstructing typical values from neighboring entries to replace only significantly affected data points.