Unmixing Spread Estimation Based on Residual Model in Spectral Flow Cytometry

Abstract

Advances in spectral flow cytometry have enabled the simultaneous measurement of dozens of markers across millions of cells within a single experiment. Despite the increasing maximum perplexity achievable in spectral panels, panel design remains constrained. A central obstacle is signal spread-- unmixed fluorescence signal misattributed to unrelated channels--which reduces the resolution of cell populations. Here we introduce the Residual Model, a robust, scalable, and interpretable model-based approach for spread prediction during panel design. The Residual Model integrates statistical features derived from single-color controls and predicts spread under Ordinary Least Squares unmixing, the most widely used unmixing method. We demonstrate its reliable predictive performance across 141 single-color control samples measured on two instruments. To facilitate practical application, we developed the USERM R package, which implements the Residual Model and provides an out-of-box solution for interactive spread prediction and visualization.