CITE-Viz: Reproducing The Flow Cytometry Workflow Using CITE-Seq Data
Channel: R Consortium
Date: 2022-10-05

Garth Kong, with Oregon Health and Science University, gave a workshop at the BioConductor Conference 2022. Kong’s workshop was on CITE-Viz: Reproducing The Flow Cytometry Workflow Using CITE-Seq Data. 

From the Bioconductor Conference 2022

Author(s): Garth Kong, Thai T Nguyen, Wesley K Rosales, Anjali D Panikar, John H W Cheney, Brittany M Curtiss, Sarah A Carratt, Theodore P Braun, Julia E Maxson
Affiliation(s): Oregon Health and Science

One of the most essential steps in single-cell sequencing analysis is the classification of cell clusters. The identification of cluster identities is essential to set up downstream analyses (e.g. differential gene expression, trajectory inference), which directly affects data interpretation. As single-cell sequencing becomes increasingly popular, there is a growing need for efficient, reproducible, and user-friendly methods to classify cell clusters. Current cell cluster classification methods include the mapping workflow used by Seurat (Hao et al. 2021), machine learning (ML) algorithms (Alquicira-Hernandez et al. 2019; Pliner et al. 2019), and identification of differentially expressed genes per cluster; however, the mapping workflow disrupts the original dimension reduction topology and confounds data interpretation. ML methods require large training datasets and need extensive computational proficiency. Lastly, manual interpretation of differential cluster expression is often time-intensive, not reproducible, and requires extensive prior biological knowledge. The nontrivial effort needed to classify cell clusters is relevant to both single and multi-omic sequencing assays such as CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing), which measures both RNA and surface protein markers (Stoeckius et al. 2017). To facilitate the classification of cell cluster identity in CITE-Seq data, we developed CITE-Viz which replicates a familiar and intuitive the flow cytometry gating workflow. In real time, users can iteratively subset cell populations of interest using the surface proteins and see those cells reflected in dimension reduction space (PCA, tSNE, UMAP, etc.). Conversely, users can highlight cells in dimension reduction space and quickly locate them in a gate window. Furthermore, CITE-Viz provides a basic quality control page for users to quickly assess the integrity of CITE-Seq datasets. In conclusion, CITE-Viz is an interactive tool that mimics the flow cytometry gating process to facilitate cell cluster classification in CITE-Seq data.

Package demo details
https://gartician.github.io/CITEVizWorkshop/ 
Source code
https://github.com/gartician/CITEVizWorkshop 

More Resources 

Bioconductor Conference Site: https://bioc2022.bioconductor.org/ 
BioC2022 Github: https://github.com/Bioconductor/BioC2022 

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