AUTHOR=Zhao Xinye , Du Alexander , Qiu Peng 

TITLE=scMODD: A model-driven algorithm for doublet identification in single-cell RNA-sequencing data

JOURNAL=Frontiers in Systems Biology

VOLUME=2

YEAR=2023

URL=https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2022.1082309

DOI=10.3389/fsysb.2022.1082309

ISSN=2674-0702

ABSTRACT=<p>Single-cell RNA sequencing (scRNA-seq) data often contain doublets, where a doublet manifests as 1 cell barcode that corresponds to combined gene expression of two or more cells. Existence of doublets can lead to spurious biological interpretations. Here, we present <bold>s</bold>ingle-<bold>c</bold>ell <bold>MO</bold>del-driven <bold>D</bold>oublet <bold>D</bold>etection (<bold>scMODD</bold>), a model-driven algorithm to detect doublets in scRNA-seq data. ScMODD achieved similar performance compared to existing doublet detection algorithms which are primarily data-driven, showing the promise of model-driven approach for doublet detection. When implementing scMODD in simulated and real scRNA-seq data, we tested both the negative binomial (NB) model and the zero-inflated negative binomial (ZINB) model to serve as the underlying statistical model for scRNA-seq count data, and observed that incorporating zero inflation did not improve detection performance, suggesting that consideration of zero inflation is not necessary in the context of doublet detection in scRNA-seq.</p>