scClustViz – Single-cell RNAseq cluster assessment and visualization

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data.

Researchers from the University of Toronto describe the R Shiny software tool , scClustViz, which provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise.

Visualizations of the data and its metadata

rna-seq

A. A 2D projection of cells in gene expression space (frequently a tSNE plot) is coloured by cluster. Clusters can be labelled by number, or automatically annotated as seen here. B. An example of a metadata overlay on the cell projection. The library size (number of transcripts detected) per cell is represented by colour scale, where darker cells have larger library sizes. C. Metadata can be represented as a scatter plot. The relationship between gene detection rate (number of unique genes detected – y-axis) and library size (x-axis) is shown here. The cells from the selected cluster (cluster 8, cortical precursors) are highlighted in red. D. Categorical metadata is represented as a stacked bar plot showing the number of cells contributing to each category per cluster. This plot shows predicted cell cycle state, with G1 phase in green, G2/M in orange, and S phase in purple

Availability – scClustViz is available at https://baderlab.github.io/scClustViz/.

Innes BT, Bader GD. (2019) scClustViz – Single-cell RNAseq cluster assessment and visualization. F1000Res [Epub ahead of print]. [article]

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