Interactive demos

Self-contained HTML viewers showing what sceleto produces on real datasets. Click an icls cluster on the UMAP, then click a gene cell in the marker comparison heatmap (or use the chip strip / mouse wheel / ←/→) to drive the edge-activation graph on the bottom-right.

Built from sceleto.markers.HierarchyRun.interactive_viewer() (or the lower-level build_branching_html) combined with a sceleto.markers.marker() graph run on icls.

PDAC fibroblast

Non-batch hierarchy viewer

Three Leiden resolutions (0.1 / 0.5 / 1.0). Heatmap shows top-10 marker presence per resolution. Edge-activation graph at bottom-right colours each edge of the icls graph by per-gene FC bin.

Batch hierarchy viewer

Same dataset built with batch_key="Sample". Heatmap cells are split into per-batch expression strips; the edge-activation graph is identical to the non-batch viewer.

How to reproduce

import scanpy as sc
import sceleto as scl

adata = sc.read_h5ad("PCI01.v43.fibrawt_final_02.h5ad")

levels = ["leiden_0.1", "leiden_0.5", "leiden_1.0"]
for lv in levels:
    if lv not in adata.obs.columns:
        res = float(lv.split("_")[1])
        sc.tl.leiden(adata, resolution=res, key_added=lv)

marker_runs = {lv: scl.markers.marker(adata, lv) for lv in levels}
hr = scl.markers.hierarchy(adata, marker_runs.values(), n_top_markers=10)
mgr = scl.markers.marker(adata, "icls")
hr.interactive_viewer(adata, mgr, save="viewer.html", n_top=10)

For the batch variant, pass batch_key="Sample" to sceleto.markers.hierarchy().