SIGCell: Sketched Isotropic Gaussianization of Cell Tokens for Transferable Cytometry Set Encoder

Cytometry measures dozens of protein markers on hundreds of thousands of single cells in a blood sample and is a standard tool for profiling the human immune system. However, even with the same marker panel, cytometry data varies greatly across institutions, so a model trained at one institution does not transfer smoothly to another. Existing pretrained cytometry encoders have largely been evaluated within the data they were trained on, or fine-tuned anew with labels for each downstream dataset, so whether a frozen encoder transfers to another institution remains open. We introduce a cell-SIGReg loss term that improves the transferability of set encoders, and present SIGCell (Sketched Isotropic Gaussianized Cell set encoder), a transferable cytometry set encoder trained with it. We identify a sample-context bias in masked set encoders: a cell's token depends strongly on which sample it is encoded with, which is associated with poor transfer of source-fitted cell-type read-outs across samples and institutions. Cell-SIGReg applies the sketched isotropic Gaussian regularizer to the cell tokens pooled over a mini-batch of samples, penalizing the separating structure between samples. We benchmark SIGCell against other pretrained cytometry encoders and classical baseline algorithms. On external datasets, SIGCell improves COVID-19 severity grading (balanced accuracy $.501$, against at most $.441$ for the other frozen representations) and transfers a source-fitted COVID-19 detection rule across institutions without target-cohort labels (AUROC $.949$ and $.995$ on two external datasets, first among ten models). Ablations and token-level analyses show that the gain is associated with a much weaker sample-context bias: the share of cell-token variance that lies between samples falls from $.93$–$.98$ to $.12$–$.24$. Cell-level latent regularization is thus a simple way to improve cross-institution transfer in cytometry set encoders.