Did you just run a clustering algorithm on some single-cell RNAseq or spatial transcriptomic data (e.g. Slide-seq)? Are you trying to interrogate the cluster assignments and interpret them in light of known marker genes? Try the interactive visualization code in this repo!
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Highlight each cluster across different 2D representations of the data:
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Annotate clusters by intersecting the expression of a gene with the cluster labels:
- Select a gene of interest (either select from a dropdown menu or type in a gene name);
- Pay special attention to the bar plot on the very right -- it shows the enrichment of the cluster labels in the beads expressing the gene of interest making it easy to find the biological identity of a cluster;
- Toggle between binary expression and color proportional to gene counts (gene count per bead info available by hovering the mouse tooltip);
- Customizable lower bound on expression via a slider.
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Select an area on one plot and watch it condition the rest of the plots on the selection:
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Select points on one plot and see where they fall on the other plot:
Remark: The interactive visualizations in this repo utilize Altair and ipywidgets and are broadly applicable to any high dimensional dataset where one wishes to examine cluster labels in a two dimensional representation and overlay the label information with the value of (informative) features.
Coming soon! Turn the notebook into an interactive dashboard with Voilà.
There are two options:
- Locally
Note: This requires standard scientific Python 3 environment. A simple way of getting that is installing Anaconda.
Run the following commands in your terminal:
git clone https://github.com/tudaga/interactive_gene_plots
cd interactive_gene_plots
jupyter notebook interactive_plots_scRNAseq_Slideseq.ipynb
- Remotely via Google Colab
Don't want to install anything, download the data or clone the repo? No problem! Click on 
The notebook interactive_plots_scRNAseq_Slideseq.ipynb goes over a Slide-seq cerebellum example. The content is:
- Run a standard scanpy clustering pipeline.
- Explore the clustering outcome with standard non-interactive plots.
- Explore it with interactive plots! :)
You can download more Slide-seq datasets here.