flowTrans

flowTrans optimizes parameter estimation for flow cytometry data transformations to improve preprocessing, visualization, and automated gating accuracy.

Key Features:

• Data Transformation Optimization: Optimizes parameters for generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox transformations to accommodate wide fluorescence intensity ranges and skewed distributions.
• Maximum Likelihood Estimation: Determines optimal transformation parameters using maximum likelihood criteria based on modeling assumptions about the transformed data.
• Performance Evaluation: Compares parameter-optimized and default-parameter transformations on real and simulated flow cytometry datasets, assessing consistency of cell population locations and automated gating accuracy in scatter and fluorescence channels.
• Improved Visualization and Gating: Reduces variability and misclassification in cell population detection through optimized transformations, enhancing visualization and gating reliability.
• Data-Specific Transformation Choices: Provides data-specific guidance, favoring parameter-optimized biexponential or generalized Box-Cox for fluorescence channels and optimized hyperbolic arcsine for scatter channels while noting dataset-dependent variation.

Scientific Applications:

• High-throughput flow cytometry preprocessing: Optimizes preprocessing steps for high-throughput flow cytometry datasets to support downstream analyses.
• Automated gating and population identification: Improves accuracy and consistency of automated gating and cell population identification across samples.
• Immunology and cancer research: Supports analyses in immunology, cancer research, and other fields requiring precise cellular characterization by improving transformation fidelity.

Methodology:

Implemented within the Bioconductor ecosystem using the flowCore package; performs parameter optimization for generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox transformations using maximum likelihood estimation, with evaluation on real and simulated flow cytometry datasets and modeling assumptions applied to transformed data.