SPECK: An Unsupervised Learning Approach for Cell Surface Receptor Abundance Estimation for Single Cell RNA-Sequencing Data

The rapid development of single cell transcriptomics has revolutionized the study of complex tissues. Single cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a tissue sample, enabling researchers to identify cell types, phenotypes and interactions that control tissue structure and function. A key requirement of these applications is the accurate estimation of cell surface protein abundance. Although technologies to directly quantify surface proteins are available, this data is uncommon and limited to proteins with available antibodies. While supervised methods that are trained on Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data can provide the best performance, this training data is also limited by available antibodies and may not exist for the tissue under investigation. In the absence of protein measurements, researchers must estimate receptor abundance from scRNA-seq data. We thereby developed a new unsupervised method for receptor abundance estimation using scRNA-seq data called SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and evaluated its performance against other unsupervised approaches on up to 215 human receptors and multiple tissue types. This analysis reveals that techniques based on a thresholded reduced rank reconstruction (RRR) of scRNA-seq data are effective for receptor abundance estimation with SPECK providing the best overall performance.