Notebook Tutorials#
Short, runnable walkthroughs. Paste the code into an R session (RStudio,
Positron, an R Markdown notebook) or open the notebooks (.ipynb, R kernel)
in Jupyter or Google Colab, and run them as they are.
Cellucid in your pipeline (notebooks, each with a Python twin)#
Show an object in one line, work in the viewer, and bring what you made back onto the Seurat or SingleCellExperiment object in one call — or push what you computed into the viewer. Each opens in Google Colab from its badge.
Topic |
R |
Python twin |
|---|---|---|
View an object in a notebook (Jupyter, Colab, RStudio) |
||
Pull highlights, labels and scores back |
Pull everything back: highlights, labels and scores (Seurat, SingleCellExperiment) |
|
Push clusters, refine, pull back ( |
Push clusters into the viewer, refine them there, pull them back |
Push clusters into the viewer, refine them there, pull them back |
No notebook: the web app’s exports in one call |
The round trip without a notebook: export from the web app, read in one call |
The round trip without a notebook: export from the web app, read in one call |
Gene sets: GMT in, signature scores out |
||
A selection, re-analysed |
||
Subsampled views |
Subsampled views: pull from a share of the cells, and reproduce it exactly |
Subsampled views: pull from a share of the cells, and reproduce it exactly |
In a Jupyter notebook R hears the viewer between cells: show the viewer in one
cell, work in it, and call cellucid_pull() in the next
(Cellucid in your pipeline: notebook round trip (Python and R)
explains the two-cell pattern and lists the calls side by side).
Walkthroughs#
Three start from a Seurat or SingleCellExperiment object (the first three cards below); three simulate their data from matrices (Beginner, Intermediate and Advanced).
cellucid_view(seu), colour and highlight from the console, and the cells you
lasso back in R.
cellucid_prepare(data = sce) to a Zarr folder, published on GitHub and
opened from one link.
A Seurat object of two assays: the Modality select, and one Cellucid-ready MuData for others.
“I just want to see my cells.” One embedding, cell metadata, one file to open.
Gene expression, gene symbols, a gene panel, launch defaults, and citation metadata.
Vector fields, a neighbour graph, and reading the store back value for value.