Interactive Marker Selection

PicturedRocks provides an interactive user interface for selecting markers that allows the practitioner to use both their domain knowledge and the information-theoretic methods to select a small set of genes.

User interface showing t-SNE heatmap

A screenshot of the Interactive Marker Selection user interface being used on the Paul dataset. The “candidate” genes are updated after any addition to or removal from the “current” list of genes. The user may also type in the name of a gene at any point.

Violin plot example

A screenshot of the violin plot option for Interactive Marker Selection.

The user can specify the tabs they want available through the visuals argument. It is also possible to write your own interface for a tab using any plotting library available to you. See below for more details.

class picturedrocks.markers.interactive.InteractiveMarkerSelection(adata, feature_selection, visuals=None, disp_genes=10, connected=True)

Run an interactive marker selection GUI inside a jupyter notebook

  • adata (anndata.AnnData) – The data to run marker selection on. If you want to restrict to a small number of genes, slice your anndata object.
  • feature_selection (picturedrocks.markers.mutualinformation.iterative.IterativeFeatureSelection) – An instance of a interative feature selection algorithm class that corresponds to adata (i.e., the column indices in feature_selection should correspond to the column indices in adata)
  • visuals (list) – List of visualizations to display. These can either be shorthands for built-in visualizations (currently “tsne”, “umap”, and “violin”), or an instance of InteractiveVisualization (see GeneHeatmap and ViolinPlot for example implementations).
  • disp_genes (int) – Number of genes to display as options (by default, number of genes plotted on the tSNE plot is 3 * disp_genes, but can be changed by setting the plot_genes property after initializing.
  • connected (bool) – Parameter to pass to plotly.offline.init_notebook_mode. If your browser does not have internet access, you should set this to False.


This class requires modules not explicitly listed as dependencies of picturedrocks. Specifically, please ensure that you have ipywidgets installed and that you use this class only inside a jupyter notebook.


Redraw jupyter widgets after a change

This is called internally and there should usually be no need for the user to explicitly call this method.


Display the jupyter widgets

class picturedrocks.markers.interactive.GeneHeatmap(dim_red='tsne', n_pcs=30)

GeneHeatmap for Interactive Marker Selection

  • dim_red (str) – Dimensionality reduction algorithm to use. Currently available options are “umap” and “tsne”
  • n_pcs (int) – The number of principal components to map to before running dimensionality reduction
class picturedrocks.markers.interactive.ViolinPlot

Violin Plots for each class label

Using Your Own Visualizations

To write your own visualization, simply extend the InteractiveVisualization. Use the source code for GeneHeatmap and ViolinPlot as a reference for implementation.

class picturedrocks.markers.interactive.InteractiveVisualization

Abstract base class for interactive visualizations

Extend this class and pass an instance of it to InteractiveMarkerSelection to use your own visualization. It is recommended that you begin your implementation of __init__ with:


You are welcome to add parameters specific to your visualization in the __init__ method.

prepare(adata, out)

Prepare for visualization

This method is called when InteractiveMarkerSelection is initialized. It is recommended that you begin your implementation with

super().prepare(adata, out)

This stores adata and out in self.adata and self.out respectively.

redraw(next_gene_inds, cur_gene_inds)

Draw the visualization

You must implement this method. To display the plots in the appropriate widget, use:

with self.out: # or your plotting library's equivalent

Title of the visualization

This should be a Python property, using the @property decorator.