ABSTRACT
Single cell RNA sequencing is a powerful tool for characterizing the molecular and cellular heterogeneity of immune cells during their development and activation. 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 that links transcriptional and cell surface protein expression profiling data. Further, we propose a quantitative metric for cluster purity of CITE-seq data, enabling effective determination of clustering algorithms and their parameters, and finally demonstrate the utility of our toolkit through seamless integration with standard single cell analysis workflows on several public datasets. Through comparison to existing multimodal methods, we demonstrate that LinQ-View generates more accurate cell clusters and is highly efficient, even with massive datasets. LinQ-View is specialized in handling CITE-seq data with routine numbers of surface protein features (e.g. less than 50), 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 COVID-19-infected patients and influenza-immunized subjects, revealing antigen-specific B cell subsets and previously unknown T cell subsets post-infection and vaccination.