Artificial-cell-type aware cell-type classification in CITE-seq.

Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping. We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq. http://github.com/QiuyuLian/CITE-sort. Supplementary data is available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.

Citation

Qiuyu Lian, Hongyi Xin, Jianzhu Ma, Liza Konnikova, Wei Chen, Jin Gu, Kong Chen. Artificial-cell-type aware cell-type classification in CITE-seq. Bioinformatics (Oxford, England). 2020 Jul 01;36(Suppl_1):i542-i550

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PMID: 32657383

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