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Yearbook

Yearbook renders a grid of cell "portraits" — small, zoomed-in spatial crops centered on individual cells, all sharing synchronized zoom/pan state while each shows a different region of the tissue. It's useful for inspecting many individual cells side by side (e.g. the top cells for a gene, or a random sample from a cluster).

What it shows

  • A grid of portraits (configurable rows/cols), each centered on one cell, with the same CELL/TRX controls and gene search as Landscape.
  • Pagination controls to page through a larger cell selection than fits on one page.
  • A query box for selecting cells by cluster and/or gene directly in the browser (see front_end_query below), independent of pagination.

Choosing which cells to show

There are three ways to choose which cells Yearbook displays, in increasing order of power:

  1. An explicit id list — cells=["cell_1", "cell_2", ...].
  2. A back-end selection — selection=..., accepting a celldega.select.Selection, a JSON-ready selection dict, or a plain id list. This is the recommended way to drive the grid from a Python AnnData object.
  3. A stateless front-end query — front_end_query=..., evaluated in the browser against the dataset's LandscapeFiles, needing only a base_url and no Python AnnData.

Usage

import celldega as dega

yb = dega.viz.Yearbook(
    base_url="https://your-landscape-files-url",
    front_end_query={"gene": "BRCA1", "max_cells": 50},
    rows=2,
    cols=2,
)
yb

Yearbook can also be linked to a Clustergram via dega.viz.spatial_clustergram, exactly like Landscape. For the full query/selection algebra and constructor arguments, see the Viz Module API reference.

Note

Screenshots and an example video are coming soon.