Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

Lian, Q.; Xin, H.; Ma, J.; Konnikova, L.; Chen, W.; Gu, J.; Chen, K.

doi:10.1101/2020.01.31.928010

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bioRxiv · 10.1101/2020.01.31.928010

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

Lian, Q.·Xin, H.·Ma, J.·Konnikova, L.·Chen, W.·Gu, J.·Chen, K.

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Abstract

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 and complicates 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 artificial cell types. 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 but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real biological-cell-type 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.

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Lian, Q., Xin, H., Ma, J., Konnikova, L., Chen, W., Gu, J., Chen, K.. 2020-02-02. Artificial-Cell-Type Aware Cell Type Classification in CITE-seq. https://doi.org/10.1101/2020.01.31.928010

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