Abstract Single-cell RNA sequencing datasets comprise true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in single-cell RNA sequencing is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed to infer true singlets and doublets, they typically rely on datasets being highly heterogeneous. Here we develop and apply singletCode, a computational framework that leverages datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground truth singlets. We demonstrate the feasibility of singlets extracted via singletCode to evaluate the performance and robustness of existing doublet detection methods. We find that existing doublet detection methods are not as sensitive as expected when tested on doublets simulated from experimentally realistic ground truth singlets. As DNA barcoded datasets are being increasingly reported, singletCode can identify singlets and inform rational choice of doublet detecting algorithms and their associated limitations.

singletCode: synthetic barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms