iMUBAC

iMUBAC integrates multi-batch high-dimensional cytometry datasets to correct batch effects and enable unified immunophenotyping and detection of aberrant cell populations across experimental batches.

Key Features:

• Integration without technical replicates: Integrates multibatch cytometry datasets without requiring technical replicates to enable cross-batch comparison.
• Batch-specific classification learning: Learns batch-specific cell-type classification boundaries by overlaying cells from multiple healthy controls across different batches.
• Unsupervised cell-type identification: Performs unsupervised cell-type identification across diverse cytometry datasets.
• Aberrant immunophenotype detection: Detects aberrant immunophenotypes within patient samples using unified classification boundaries.
• Compatibility with cytometry modalities: Demonstrated on and compatible with mass cytometry and spectral flow cytometry datasets.
• Implementation: Implemented as an R package for integration into computational analysis workflows.
• Scalability and flexibility: Provides a scalable and flexible computational framework for multibatch cytometry integration.

Scientific Applications:

• Cross-batch immunophenotyping: Enables consistent immunophenotyping across experiments conducted at different sites or time points.
• Patient aberration discovery: Identifies patient-specific aberrant immunophenotypes in clinical and research cohorts.
• Comparative cytometry analysis: Facilitates comparative analysis of public and proprietary mass cytometry and spectral flow cytometry datasets.
• Objective inter-batch comparisons: Supports objective inter-batch comparisons to reduce batch-driven biases in downstream analyses.

Methodology:

Overlaying cells from multiple healthy controls across batches to learn batch-specific cell-type classification boundaries; unsupervised cell-type identification across datasets; integration of multibatch cytometry datasets without requiring technical replicates.