Cross-Modal Normalization for MaxFuse

This skill provides a specialized workflow for aligning the scales of RNA and protein datasets during cross-modal integration, specifically targeting algorithms like MaxFuse. By enforcing consistent z-scoring and variance normalization across both modalities, it prevents high-variance features (typically RNA) from dominating the matching process. This ensures that distances computed for matching are biologically meaningful, significantly reducing false-positive cell type identifications and improving the accuracy of downstream single-cell analysis.

Características Principales

Casos de Uso