Abstract
Integrating heterogeneous datasets across different measurement platforms poses fundamental challenges for statistical inference. An important example is cell type deconvolution, where cell type proportions in bulk RNA-seq data are estimated using reference single-cell data from different sources, leading to platform-specific scaling effects, measurement noise, and biological heterogeneity. Existing methods often treat estimated proportions as observed in downstream analyses, potentially compromising validity when comparing multiple individuals. We introduce MEAD, a statistical framework for estimation and inference in deconvolution with externally approximated design matrices. We establish necessary and sufficient conditions for identifiability under arbitrary gene-specific cross-platform scaling differences and develop valid inferential procedures for both individual-level proportions and comparisons across individuals, accounting for gene–gene correlation and shared estimation uncertainty. Simulations and real-data analyses demonstrate competitive estimation accuracy and reliable statistical inference.
Lin Gui
Ph.D. student
