histomap 0.1.2

No project description provided

histomap

Napari dock widget to overlay tile polygons and table annotations from SpatialData onto an H&E image, and to interactively select cells in a 2D embedding (e.g., UMAP in AnnData.obsm) and visualize the selected regions on the H&E.

• Overlay mode: color polygons on the H&E by an AnnData.obs column (categorical or numeric) from SpatialData.tables[<name>].
• UMAP-lasso mode: open a 2D embedding from AnnData.obsm (e.g., X_umap), lasso points, and preview the corresponding tile polygons on the H&E; optionally save the selection back to obs.

---

Installation

# Recommended: use a fresh environment
conda create --name histomap python=3.11 -y
conda activate histomap

# If published on PyPI:
pip install histomap

# If installing from source in this repo:
# pip install -e .

Runtime dependencies (installed automatically if from PyPI)

• napari, PyQt5, magicgui
• geopandas, shapely
• anndata, matplotlib, spatialdata
• (Optional, for robust SVS reading) openslide-python, napari-openslide

Windows notes

• If geopandas/shapely wheels fail, upgrade pip (pip install -U pip) and try again.
• For OpenSlide, install the prebuilt binaries or use conda install -c conda-forge openslide openslide-python in the same environment.

---

Quick start

import histomap as hm

Method 1 — Use a SpatialData object

import spatialdata as sd
import histomap as hm

sda = sd.read_zarr("/path/to/spatialdata.zarr")

# Preferred: pass the H&E path explicitly (best on Windows)
viewer = hm.histomap(
    sda,
    wsi_path="/path/to/HE_image.svs",   # alias: imagePath="/path/to/HE_image.svs"
    # mpp=0.263049,                     # µm/px (optional; if tiles are in microns, scale=1/mpp is auto-applied)
)

# If you omit wsi_path, histomap will try to parse a path from str(sda).
# If no path is found, you’ll see a dialog asking you to pass wsi_path explicitly.

Typical workflow in the UI

1. Click Open WSI in Viewer (if not already opened).
2. Choose a Table (from sda.tables), Data axis (obs or obsm), and a column/key.
3. • For obs: click Render Overlay to color polygons on the H&E.
   • For obsm (e.g., X_umap): click Open UMAP + Lasso, lasso points, and inspect the green Lasso preview on the H&E.
4. Optionally enter a Layer name and obs column, then click Save selection to write labels into AnnData.obs and add a persistent overlay layer.

Method 2 — Use with lazyslide/wsidata

import lazyslide as zs
from wsidata import open_wsi
import histomap as hm

wsi = open_wsi("/path/to/HE_image.svs")

# Assuming tiling & feature extraction are already done, e.g.:
# zs.pp.tile_tissues(wsi, tile_px=256, mpp=0.5)
# zs.tl.feature_extraction(wsi, model='chief')

# Launch viewer and overlay tiles/annotations stored in wsi.tables / wsi.shapes
viewer = hm.histomap(wsi)

---

Parameters (entry point)

viewer = hm.histomap(
    sda_or_wsi,                        # SpatialData or compatible WSIData
    *,
    wsi_path=None,                     # str | Path | None; preferred image to open (alias: imagePath)
    mpp=None,                          # float | None; µm/px. If provided and no explicit scale, applies scale=(1/mpp, 1/mpp)
    global_to_pixel_scale=None,        # (sx, sy) override for polygon transform
    global_to_pixel_translate=None,    # (tx, ty) override
    theme="dark",
    canvas_bg="white",
)

Precedence

• If global_to_pixel_scale is provided, it overrides mpp.
• If wsi_path is provided, it overrides any path parsed from SpatialData.
• If neither wsi_path nor a parsable path exists, histomap shows a dialog explaining how to pass wsi_path.

---

Data assumptions

• sda.shapes["tiles"] is a GeoDataFrame with polygons; its index contains tile IDs.
• sda.tables[<name>] is an AnnData where obs_names match the tile IDs (string-matched; dtype mismatches are handled).
• For UMAP-lasso, AnnData.obsm[<key>] contains an (n_cells, 2+) embedding (e.g., X_umap).

---

Tips & alignment

• Tiles already in pixels? Don’t pass mpp. Use Calibrate (fit tiles) if needed.
• Tiles in microns? Pass mpp=<µm/px> and click Auto-align (use MPP) (or rely on the automatic scale if you didn’t override with global_to_pixel_scale).
• The overlay layers inherit the image layer’s affine, so they remain aligned across pyramid levels.

---

Saving lasso selections (what gets written)

When you click Save selection:

• The chosen obs column is created/normalized as plain Python strings (dtype=object) with no missing values.
• Only selected rows receive the provided label (string). Non-selected rows remain unchanged (empty string by default).
• A persistent overlay layer is added with the saved selection (the transient Lasso preview is removed).

Why object strings?
Writing pandas’ nullable string dtype (dtype="string") to Zarr requires opting in (anndata.settings.allow_write_nullable_strings=True). Using plain object strings avoids that requirement and is maximally portable.

---

Troubleshooting

❗️“boolean value of NA is ambiguous” during wsi.write() / SpatialData.write()

You likely have pd.NA or mixed types in AnnData.obs. Ensure no NA in string-like columns and that they are object strings.

❗️“allow_write_nullable_strings is False”

Either set:

import anndata as ad
ad.settings.allow_write_nullable_strings = True

or convert to object strings.

❗️Cannot overwrite/move files on Windows after closing the viewer

Clear layers before saving: viewer.layers.clear() and run garbage collection.

❗️No H&E appears and you see “H&E Image Missing”

Pass the image explicitly:

viewer = hm.histomap(sda, wsi_path="/absolute/path/to/HnE.svs")

---

FAQ

Q: Do I need openslide?
A: Only if you’re opening .svs/WSI formats through Napari.

Q: Can I use a custom image and MPP without modifying the dock UI?
A: Yes—pass wsi_path=... and mpp=... to hm.histomap(...).

---