A0392
Title: Statistical inference of cell-type proportions estimated from bulk expression data
Authors: Biao Cai - City University of Hong Kong (United States) [presenting]
Abstract: There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type proportions in a bulk sample. Although many methods have been proposed recently, quantifying the uncertainties associated with the estimated cell-type proportions has not been well-studied. Lack of consideration of these uncertainties can lead to missed or false findings in downstream analyses. A flexible statistical deconvolution framework is introduced that allows a general and subject-specific covariance of bulk gene expressions. Under this framework, a de-correlated constrained least squares method is proposed called DECALS that estimates cell-type proportions as well as the sampling distribution of the estimates. Simulation studies demonstrate that DECALS can accurately quantify the uncertainties in the estimated proportions, whereas other methods fail. Applying DECALS to analyze bulk gene expression data of post-mortem brain samples from the ROSMAP and GTEx projects, it is shown that taking into account the uncertainties in the estimated cell-type proportions can lead to more accurate identifications of cell-type-specific differentially expressed genes and transcripts between different subject groups, such as between Alzheimer's disease patients and controls and between males and females.
