CytoNorm

"CytoNorm" is a normalization algorithm specifically developed to address the challenges of maintaining internal consistency across clinical samples in high-dimensional flow cytometry data. Recognizing the limitations of existing cross-sample normalization methods that might overlook the single-cell nature of the data, CytoNorm introduces a robust solution that leverages shared controls across various study batches to ensure accurate and biologically relevant normalization.

Key Features and Functionalities:

- Shared Controls for Consistent Normalization: CytoNorm uses data from shared controls across study batches to learn the appropriate transformations needed for each batch, ensuring consistent data quality and comparability.

- Population-Specific Transformations: Acknowledging the technical variation influenced by the amount of protein expressed on specific cell types, CytoNorm calculates subset-specific transformations on the control samples, addressing the heterogeneity of cellular samples.

- Quantile Distribution Alignment with Splines: The algorithm identifies the overall cellular distribution through clustering and aligns the quantile distributions of control samples with splines, ensuring accurate normalization transformations.

- Batch-Specific Variation Removal: By applying the learned transformations to all other clinical samples within the same batch, CytoNorm effectively removes batch-specific variations without compromising the biological integrity of the data.