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

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 ρ = 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 ρ: 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.

Identifying which cell types manifest genetic effects on complex traits requires cell-type-specific gene expression data, but single-cell sequencing is prohibitively expensive for population genetic sample sizes. Ball et al. present CellMAGE, a computational method that delivers single-cell fidelity from bulk sequencing in multiparental populations, at bulk sequencing cost. Because each individual's genotypes are known, CellMAGE weights parental cell-type profiles by genetic composition directly, requiring no model fitting. Validated in Diversity Outbred mice across 12 brain cell types, CellMAGE predictions were highly accurate, outperformed an existing method, and apply to any multiparental population with available parental single-cell data, including crop species.