SelectBCM

SelectBCM evaluates and selects batch-correction methods for bulk transcriptome datasets to mitigate batch effects while preserving true biological signals for downstream clustering and differential expression analyses.

Key Features:

• Batch-effect prioritization: Systematically evaluates and ranks alternative batch-correction methods to identify the most suitable approach for a given bulk transcriptomic dataset.
• Improved biological clustering and differential expression: Selects correction methods that reduce technical artifacts while preserving biological structure used in clustering and gene differential expression analyses.
• Application to real-world datasets: Demonstrated on datasets related to rheumatoid arthritis, osteoarthritis, and macrophage activation states.

Scientific Applications:

• Disease biology research: Enables more reliable integration of transcriptomic experiments for studies of disease mechanisms, including rheumatoid arthritis and osteoarthritis.
• Meta-analyses of biological states: Supports integration and comparative analysis of transcriptomic data across conditions such as macrophage activation states.

Methodology:

SelectBCM evaluates a range of batch-correction methods on input bulk transcriptomic datasets and selects the method that best mitigates batch effects while preserving biological signal, judging impact on clustering and differential expression analyses.