Estimation of cell type proportions from bulk RNA-Seq of porcine whole blood samples using partial reference-free deconvolution

Abstract

Whole blood has become increasingly utilized in transcriptomic studies because it is easily accessible and can be collected from live animals with minimal invasiveness. However, whole blood represents an extremely complex mixture of cell types, and cell type proportions can confound downstream statistical analyses. Information on cell type proportions may be missing from blood transcriptome studies for a variety of reasons. Experimental approaches for cell counting, such as cell sorting, are arduous and expensive, and therefore may not feasible for studies conducted on a limited budget. Statistical deconvolution can be applied directly to transcriptomic data sets to estimate cell type proportions. In addition to being financially advantageous, computational deconvolution can readily be applied to old datasets, where it may be difficult or impossible to re-analyze for cell type information. In an effort to assist researchers in recovering cell type proportions from porcine whole blood transcriptome samples, we present a manually curated set of porcine blood cell markers that can be utilized in a partial reference-free deconvolution framework to obtain estimates of cell types measured in a standard complete blood count (CBC) panel, which includes neutrophils, lymphocytes, monocytes, eosinophils, basophils, and red blood cells.