Robust Statistical Inference for Cell Type Deconvolution


Cell type deconvolution is a computational approach to infer proportions of individual cell types from bulk transcriptomics data. Though many new methods have been developed for cell type deconvolution, most of them only provide point estimation of the cell type proportions. On the other hand, estimates of the cell type proportions can be very noisy due to various sources of bias and randomness, and ignoring their uncertainty may greatly affect the validity of downstream analyses. In this paper, we propose a comprehensive statistical framework for cell type deconvolution and construct asymptotically valid confidence intervals both for each individual’s cell type proportion and for quantifying how cell type proportions change across multiple bulk individuals in downstream regression analyses. Our analysis takes into account various factors including the biological randomness of gene expressions across cells and individuals, gene-gene dependence, and the cross-platform biases and sequencing errors, and avoids any parametric assumptions on the data distributions. We also provide identification conditions of the cell type proportions when there are arbitrary platforms-specific biases across sequencing technologies.

Jingshu Wang
Jingshu Wang
Assistant Professor in Statistics