Limits of marker information transfer]{Limits of marker information transfer in cross-panel cytometry imputation
- 1University of Pennsylvania
Abstract
Marker imputation can bridge cytometry datasets acquired with different panels and platforms. Its validity depends on whether common markers retain information about unmeasured target markers and whether those relationships transfer across assay contexts. We evaluated these limits in two paired flow cytometry-CyTOF cohorts comprising 134 patients and 257 specimens. A patient-held-out framework distinguished within-platform same-cell reconstruction from cross-platform distribution translation without cell correspondence. We compared conventional machine-learning and deep generative estimators with median imputation and used permutation controls to estimate the contributions of patient matching and source-exclusive markers. Evaluation covered marker values, specimen distributions, phenotype utility, and directional predictive relationships among markers. Within platforms, exact k-nearest neighbors reduced mean absolute error by 12.8-35.0% relative to median imputation. However, the median-based reconstructions were contracted, with a median ratio of predicted to observed interquartile range below 1 in every context and evaluable population. Patient matching improved cross-platform distribution recovery, whereas adding source-exclusive markers or higher-capacity estimators did not consistently produce net gains. In the independently annotated acute myeloid leukemia (AML) cohort, reconstructed markers largely preserved broad-lineage classification but did not recover fine-phenotype information available through direct measurement. In AML-CyTOF, the measured full panel outperformed the reconstructed representation by 10.25 percentage points for fine phenotypes. Predictive relationships among markers were context-specific. Most targets were better explained by several markers than by one. Marker selection based on these relationships showed a trade-off between panel size and pairwise coverage. Paired samples and imputation can therefore connect selected marker distributions across panels but should not be treated as substitutes for direct measurement. Cross-panel cytometry should be evaluated with patient-held-out tests matched to the resolution of available ground truth and the intended downstream use.
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BibTeX
@misc{lee2026limits,
title = {{Limits of marker information transfer]Limits of marker information transfer in cross-panel cytometry imputation}},
author = {J. Lee and A. Nair and C.-U. Jeong and M. Ionita and J. Joo and M. E. Lee and L. Takemaru and Y. Nam and J. Kim and B. Lee and M. L. McKeague and S. Verma and A. Rezk and D. Smith and S. A. Apostolidis and A. E. Baxter and M. P. Carroll and A. R. Greenplate and E. J. Wherry and D. Kim},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.07.757120},
url = {https://www.biorxiv.org/content/10.64898/2026.10.07.757120v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Limits of marker information transfer]{Limits of marker information transfer in cross-panel cytometry imputation
AU - J. Lee
AU - A. Nair
AU - C.-U. Jeong
AU - M. Ionita
AU - J. Joo
AU - M. E. Lee
AU - L. Takemaru
AU - Y. Nam
AU - J. Kim
AU - B. Lee
AU - M. L. McKeague
AU - S. Verma
AU - A. Rezk
AU - D. Smith
AU - S. A. Apostolidis
AU - A. E. Baxter
AU - M. P. Carroll
AU - A. R. Greenplate
AU - E. J. Wherry
AU - D. Kim
PY - 2026
DA - 2026/10/07
PB - bioRxiv
DO - 10.64898/2026.10.07.757120
UR - https://www.biorxiv.org/content/10.64898/2026.10.07.757120v1
AB - Marker imputation can bridge cytometry datasets acquired with different panels and platforms. Its validity depends on whether common markers retain information about unmeasured target markers and whether those relationships transfer across assay contexts. We evaluated these limits in two paired flow cytometry-CyTOF cohorts comprising 134 patients and 257 specimens. A patient-held-out framework distinguished within-platform same-cell reconstruction from cross-platform distribution translation without cell correspondence. We compared conventional machine-learning and deep generative estimators with median imputation and used permutation controls to estimate the contributions of patient matching and source-exclusive markers. Evaluation covered marker values, specimen distributions, phenotype utility, and directional predictive relationships among markers. Within platforms, exact k-nearest neighbors reduced mean absolute error by 12.8-35.0% relative to median imputation. However, the median-based reconstructions were contracted, with a median ratio of predicted to observed interquartile range below 1 in every context and evaluable population. Patient matching improved cross-platform distribution recovery, whereas adding source-exclusive markers or higher-capacity estimators did not consistently produce net gains. In the independently annotated acute myeloid leukemia (AML) cohort, reconstructed markers largely preserved broad-lineage classification but did not recover fine-phenotype information available through direct measurement. In AML-CyTOF, the measured full panel outperformed the reconstructed representation by 10.25 percentage points for fine phenotypes. Predictive relationships among markers were context-specific. Most targets were better explained by several markers than by one. Marker selection based on these relationships showed a trade-off between panel size and pairwise coverage. Paired samples and imputation can therefore connect selected marker distributions across panels but should not be treated as substitutes for direct measurement. Cross-panel cytometry should be evaluated with patient-held-out tests matched to the resolution of available ground truth and the intended downstream use.
ER -