Interactive gene plots

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!

Demos

• Highlight each cluster across different 2D representations of the data:

• 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.

• Select an area on one plot and watch it condition the rest of the plots on the selection:

• 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.

How do I run this?

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 .

Intro

The notebook interactive_plots_scRNAseq_Slideseq.ipynb goes over a Slide-seq cerebellum example. The content is:

1. Run a standard scanpy clustering pipeline.
2. Explore the clustering outcome with standard non-interactive plots.
3. Explore it with interactive plots! :)

You can download more Slide-seq datasets here.