CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression

Authors

Ball, R. L., Klein, A., Auth, A. A., Skelly, D. A., He, H., Philip, V. M., Gagnon, L. H., Chesler, E. J.

Abstract

Single-cell RNA-sequencing remains prohibitively expensive for multiparental population (MPP) studies. Existing deconvolution methods treat bulk RNA-seq as genetically anonymous mixtures, but in MPPs, the proportional contribution of each parental strain to each progeny's transcriptome is already known. CellMAGE (Cell-type deconvolution for Multi-parent Analysis of Gene Expression) weights parental cell-type profiles by each progeny's known genetic composition, requiring no model training and no minimum sample size. Validated in 16 Diversity Outbred mice across 12 prefrontal cortex cell types and 23,116 genes, predicted and measured gene expression were statistically equivalent (0.05) in all cell types (pooled Spearman = 0.923, 95% CI: 0.910, 0.934). CIBERSORTx required 96 additional samples to resolve at most 12.4% of genes and only 3 cell types; CellMAGE outperformed it even within this restricted comparison (per-cell-type median : 0.908-0.974 vs. 0.353-0.641). CellMAGE is applicable to any MPP with parental single-cell data, including diploid crop MAGIC populations.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 13 Sep 2026.

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