Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach

Interest in studying genomics and transcriptomics at the single-cell level has been increasing. One of the keys to single-cell study is developing cell-sorting technology to separate cells according to their type. However, the process of cell isolation changes the cell microenvironment that affects gene activity, and this change in gene expression can affect the conclusion of the single-cell study. To address this, we propose a novel PEnalized deconvolution Analysis for Cell separation-induced Heterogeneity (PEACH). By adopting a Bayesian variable selection scheme, PEACH can simultaneously decompose cell-type-specific expression from bulk tissue and identify cell separation-induced differential expression (CSI-DE) genes. We validated PEACH by using four benchmark datasets and one in silico mixture dataset. In the real application, we used PEACH to analyze an immune-related disease dataset, a blood dataset, and a skin dataset, and we consistently identified immediate-early genes, ribosomal protein genes, and mitochondrial genes across the three datasets. Our study illustrates that genes sensitive to the cell-sorting process are biologically meaningful and nonnegligible, and it may provide new insights into single-cell studies for transcriptomic analysis. The model has been implemented in the R package “PEACH,” and the algorithm is available for download.