The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, CITE-Seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) simultaneously profiles the single-cell RNA transcriptome and the surface protein expression. The extra dimension of surface protein markers can be used to further identify cell clusters – an essential step for downstream analyses and interpretation. Additionally, multi-dimensional datasets like CITE-Seq require nuanced visualization methods to accurately assess the data. To facilitate cell cluster classification and visualization in CITE-Seq, we developed CITEViz.
CITEViz is a single-cell visualization platform with a custom module that replicates the interactive flow-cytometry gating workflow. With CITEViz, users can investigate CITE-Seq specific quality control (QC) metrics, view multi-omic co-expression feature plots, and classify cell clusters by iteratively gating on the abundance of cell surface markers. CITEViz was developed to make multi-modal single-cell analysis accessible to a wide variety of biologists, with the aim to discover new insights into their data and to facilitate novel hypothesis generation. If you use our package, please cite our paper: CITE-Viz: Replicating the Interactive Flow Cytometry Workflow in CITE-Seq
We recommend installing CITEViz through the RStudio IDE using the following commands:
devtools::install_github("maxsonBraunLab/CITEViz")
library(CITEViz)
run_app()CITEViz installation is currently facilitated with devtools, and the development team is working hard to submit the package to the Comprehensive R Archive Network (CRAN).
Arguably the most important feature for CITEViz, users can gate on a set of features (ADT, RNA, etc.) to subset cells in the dimensional reduction space (e.g. UMAP, PCA, tSNE). Users can use one or multiple layers of gates to enrich for specific cell populations justl like flow cytometry. In the following example, we demonstrate a 2-layer gate to get CD11b-1 CD45-1 cells (mixture of myeloid and lymphoid cells) and CD8+ CD4- cells to get CD8 T cells:
- Select cells with relatively low levels of CD11b-1 and CD45-1.
- From the cells in the first gate, select cells with low levels of CD4 and high levels of CD8.
Unique to CITEViz, users can 'back-gate' on a selection of cells, and highlight them in feature space. We believe this feature can highlight cells in feature space from a "labels-first" or "top-down" workflow. In the following example, we back-gate on a selection of B-cells:
A set of quality control metrics can be assessed in CITEViz, split by any categorical metadata in the input Seurat object. This feature helps users identify potential faulty samples or batch effect. In the following example, CITEViz clearly displayed a difference in the Number of Unique ADTs between patient donors P1-P4 and P5-P8.
Co-Expression of features greatly facilitates a holistic view of single-cell multi-omic datasets. For example, this co-expression plot of CD14 and CD16 protein expression show cell population heterogeneity in monocytes in the left cluster. Most monocytes have high CD14 cells and moderate CD16, except for a specific population at the bottom dip that have high CD16 and low CD14.
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