Xoublet: An Extreme-Scale Doublet Detection Algorithm for Cardiac Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of cardiac cell composition and its underlying pathogenesis. In scRNA-seq data analysis, doublet detection plays a pivotal role in quality control. However, these methods often face challenges related to high computational demands and extended runtimes, particularly in large-scale datasets and rare cell identification. To address these issues, this study introduces the eXtreme-scale Doublet Analyzer (Xoublet) algorithm, which projects single-cell transcriptomic data into principal component analysis space and simulates doublets. Xoublet constructs a hierarchical tree to facilitate rapid screening of similar data, expediting neighborhood searches. Xoublet was compared with Scrublet and DoubletFinder on human-mouse and Cell Hashing datasets with experimentally derived labels, while method-specific calls were examined in an annotated cardiac atlas without independent doublet ground truth. Computational scalability was evaluated separately on HeartMap. Accuracy varied across datasets: Xoublet performed similarly to Scrublet in the cell-line Cell Hashing benchmark but was less discriminative in the human-mouse and peripheral blood mononuclear cell benchmarks. On 2,479,674 HeartMap profiles, Xoublet completed scoring in 574.8 seconds with 4.14 GiB peak resident memory, compared with 1060.6 seconds and 22.81 GiB for the Scrublet core. These findings indicate that Xoublet provides computational advantages for large-scale scoring, although its detection accuracy and sensitivity to specific cell states remain data-dependent.