1. Improved integration of single-cell transcriptome and surface protein expression by LinQ-View
- Author
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Lei Li, Haley L. Dugan, Christopher T. Stamper, Linda Yu-Ling Lan, Nicholas W. Asby, Matthew Knight, Olivia Stovicek, Nai-Ying Zheng, Maria Lucia Madariaga, Kumaran Shanmugarajah, Maud O. Jansen, Siriruk Changrob, Henry A. Utset, Carole Henry, Christopher Nelson, Robert P. Jedrzejczak, Daved H. Fremont, Andrzej Joachimiak, Florian Krammer, Jun Huang, Aly A. Khan, and Patrick C. Wilson
- Subjects
scRNA-seq ,CITE-seq ,multimodal method ,integrated model ,purity metric ,computational method ,Biotechnology ,TP248.13-248.65 ,Biochemistry ,QD415-436 ,Science - Abstract
Summary: Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells). Motivation: Multimodal single-cell sequencing enables multiple aspects for characterizing the dynamics of cell states and developmental processes. Properly integrating information from multiple modalities is a crucial step for interpreting cell heterogeneity. Here, we present LinQ-View, a computational workflow that provides an effective solution for integrating multiple modalities of CITE-seq data for downstream interpretation. LinQ-View balances information from multiple modalities to achieve accurate clustering results and is specialized in handling CITE-seq data with routine numbers of surface protein features.
- Published
- 2021
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