  Cell type deconvolution is a computational method that estimates the
proportions of different cell types within bulk transcriptomics data by
leveraging information from reference single-cell RNA sequencing data. Despite
its origin as a simple linear regression model, this approach faces challenges
due to technical and biological variability and biases between the bulk and
single-cell datasets. While several new methods have been developed, most only
provide point estimates of cell type proportions, neglecting the uncertainty
inherent in these estimates. Consequently, false positives can arise when
comparing changes in cell type proportions across multiple individuals. In this
paper, we introduce MEAD, a comprehensive statistical framework for efficient
cell type deconvolution. Our approach constructs asymptotically valid
confidence intervals for individual cell type proportions, as well as for
quantifying changes in cell type proportions across multiple individuals. Our
analysis accounts for factors such as biological variability in gene
expressions, gene-gene dependence, cross-platform biases, and sequencing
errors, without relying on parametric assumptions about the data distributions.
Moreover, we establish necessary and sufficient conditions for identifying cell
type proportions in the presence of platform-specific biases across sequencing
technologies.
