UnBlender: validating individual analyses in respiratory bulk RNA-seq cell type deconvolution

Abstract

Analysis of RNA-seq data of respiratory samples has contributed much to our understanding of lung disease. However, bulk RNA-seq data are dependent on both cell type composition and the transcriptional activity of these samples constituent cells, which complicates interpretation. Cell type deconvolution is frequently used to estimate cell type proportions of bulk transcriptomic gene expression data and improve interpretation of bulk transcriptomics data. However, accuracy of the estimated cell type proportions reported after deconvolution is unknown, which may have a negative impact on the validity of the conclusions drawn. Here, we present UnBlender, a pipeline that enables respiratory scientists to perform cell type deconvolution and routinely evaluate deconvolution accuracy of their approach. UnBlender allows for custom cell type deconvolution tailored to the research question at hand, using consensus cell type labels and validating the approach to promote accurate, reproducible results.