OpenClaw-Medical-Skills spatial-transcriptomics-tutorials-with-omicverse

Guide users through omicverse's spatial transcriptomics tutorials covering preprocessing, deconvolution, and downstream modelling workflows across Visium, Visium HD, Stereo-seq, and Slide-seq datasets.

install

source · Clone the upstream repo

git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/spatial-tutorials" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-spatial-transcriptomics-tutorials-wi && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/spatial-tutorials" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-spatial-transcriptomics-tutorials-wi && rm -rf "$T"

manifest: skills/spatial-tutorials/SKILL.md

Spatial transcriptomics tutorials with omicverse

Overview

Use this skill to navigate the spatial analysis tutorials located under

Tutorials-space

. The notebooks span preprocessing utilities (

t_crop_rotate.ipynb

t_cellpose.ipynb

), deconvolution frameworks (

t_decov.ipynb

t_starfysh.ipynb

), and downstream spatial modelling or integration tasks (

t_cluster_space.ipynb

t_staligner.ipynb

t_spaceflow.ipynb

t_commot_flowsig.ipynb

t_gaston.ipynb

t_slat.ipynb

t_stt.ipynb

). Follow the staged instructions below to match the "Preprocess", "Deconvolution", and "Downstream" groupings presented in the notebooks.

Instructions

Preprocess

Load spatial slides and manipulate coordinates

Import

omicverse as ov

scanpy as sc

, and enable plotting defaults with

ov.plot_set()

ov.plot_set(font_path='Arial')

t_crop_rotate.ipynb

Fetch public Visium data via
```
sc.datasets.visium_sge(...)
```
, inspect
```
adata.obsm['spatial']
```
, and respect
```
uns['spatial'][library_id]['scalefactors']
```
when rescaling coordinates for high-resolution overlays.

Apply region selection and alignment helpers:

ov.space.crop_space_visium(...)

for bounding-box crops,

ov.space.rotate_space_visium(...)

followed by

ov.space.map_spatial_auto(..., method='phase')

, and refine offsets with

ov.space.map_spatial_manual(...)

before plotting using

sc.pl.embedding(..., basis='spatial')

Segment Visium HD tiles into cells

Organise Visium HD outputs (binned parquet counts,
```
.btf
```
histology) and load them through
```
ov.space.read_visium_10x(path, source_image_path=...)
```
.
```
t_cellpose.ipynb
```

Filter sparse bins (

ov.pp.filter_genes(..., min_cells=3)

and

ov.pp.filter_cells(..., min_counts=1)

) prior to segmentation.

Run nucleus/cell segmentation variants:

ov.space.visium_10x_hd_cellpose_he(...)

for H&E,

ov.space.visium_10x_hd_cellpose_expand(...)

to grow labels across neighbouring bins, and

ov.space.visium_10x_hd_cellpose_gex(...)

for gene-expression driven seeds. Harmonise labels with

ov.space.salvage_secondary_labels(...)

and aggregate to cell-level AnnData using

ov.space.bin2cell(..., labels_key='labels_joint')

Initial QC for downstream tasks
- For Visium/DLPFC re-analyses, compute QC metrics (
```
sc.pp.calculate_qc_metrics(adata, inplace=True)
```
  ) and persist intermediate AnnData snapshots (
```
adata.write('data/cluster_svg.h5ad', compression='gzip')
```
  ) for reuse across tutorials.
```
t_cluster_space.ipynb
```

Deconvolution

Configure single-cell references and spatial targets
- Load scRNA-seq references (
```
adata_sc = ov.read('data/sc.h5ad')
```
  ) with harmonised gene IDs and spatial slides (
```
adata_sp = sc.datasets.visium_sge(...)
```
  ).
```
t_decov.ipynb
```
- Instantiate the unified wrapper
```
ov.space.Deconvolution(...)
```
  , passing shared keys like
```
celltype_key
```
  ,
```
adata_sc
```
  , and
```
adata_sp
```
  .

Execute Tangram and cell2location pipelines

Call

decov_obj.preprocess_sc(...)

decov_obj.preprocess_sp(...)

to align matrices, then run

decov_obj.deconvolution(method='tangram', ...)

and persist outputs with

ov.utils.save(...)

plus

.write(...)

hooks for AnnData members.

For cell2location, reinitialise

ov.space.Deconvolution(..., method='cell2location')

, train (

decov_obj.deconvolution(max_epochs=...)

), monitor via

decov_obj.mod_sc.plot_history(...)

, and store models (

decov_obj.save_model(...)

Visualise inferred proportions using

ov.space.plot_cell2location(...)

sc.pl.spatial(..., color=list_of_celltypes)

, and ROI-focused pie charts after cropping (

ov.space.crop_space_visium(...)

Run Starfysh archetypal deconvolution
- Import Starfysh utilities (
```
from omicverse.external.starfysh import AA, utils, plot_utils, post_analysis
```
  ) and prepare expression counts plus optional signature sets.
```
t_starfysh.ipynb
```
- Identify anchor spots with
```
utils.prepare_data(...)
```
  , optionally infer archetypes via
```
AA.ArchetypalAnalysis(...)
```
  , and refine signatures using
```
utils.refine_anchors(...)
```
  .
- Train Starfysh models (
```
utils.run_starfysh(poe=False, ...)
```
  or
```
poe=True
```
  with histology) across multiple restarts, then parse outputs through
```
post_analysis.load_model(...)
```
  ,
```
plot_utils.pl_spatial_inf_feature(...)
```
  , and
```
cell2proportion(...)
```
  for per-cell-type maps.

Downstream

Spatial clustering and denoising
- Generate embeddings using omicverse wrappers:
```
ov.utils.cluster(..., use_rep='graphst|original|X_pca', method='mclust')
```
  ,
```
ov.space.merge_cluster(...)
```
  , and evaluate ARI (
```
adjusted_rand_score(...)
```
  ).
```
t_cluster_space.ipynb
```
- Explore algorithm-specific toggles: GraphST/BINARY require precalculated latent spaces, STAGATE training (
```
ov.utils.cluster(..., use_rep='STAGATE', ...)
```
  ), and CAST for multi-slice single-cell resolution data.

Integrate multi-slice datasets

Concatenate Stereo-seq/Slide-seqV2 batches (

ad.concat(Batch_list, label='slice_name', keys=section_ids)

) and initialise

ov.space.pySTAligner(...)

t_staligner.ipynb

Train with

STAligner_obj.train_STAligner_subgraph(...)

, call

STAligner_obj.train()

, and retrieve latent embeddings via

STAligner_obj.predicted()

before clustering (

sc.pp.neighbors(..., use_rep='STAligner')

ov.utils.cluster(...)

Model spatial gradients and trajectories

For pseudo-spatial maps, build

sf_obj = ov.space.pySpaceFlow(adata)

and train using

sf_obj.train(spatial_regularization_strength=0.1, ...)

, then compute

sf_obj.cal_pSM(...)

to populate

adata.obs['pSM_spaceflow']

t_spaceflow.ipynb

Analyse transition dynamics with

STT_obj = ov.space.STT(adata, spatial_loc='xy_loc', region='Region')

, followed by

STT_obj.train(...)

STT_obj.stage_estimate()

, and downstream visualisations (

STT_obj.plot_pathway(...)

STT_obj.infer_lineage(...)

t_stt.ipynb

Infer communication and flow networks

Pull ligand–receptor resources via

ov.external.commot.pp.ligand_receptor_database(species='human')

, filter with

filter_lr_database(...)

, and compute signaling using

ov.external.commot.tl.spatial_communication(...)

t_commot_flowsig.ipynb

Construct FlowSig inputs (
```
adata.layers['normalized'] = adata.X.copy()
```
,
```
ov.external.flowsig.tl.construct_intercellular_flow_network(...)
```
), retain spatially informative modules (Moran’s I filtering), and validate edges through bootstrapping thresholds (
```
edge_threshold = 0.7
```
).

Extract structural layers and align developmental slices
- Train GASTON with
```
gas_obj = ov.space.GASTON(adata)
```
  , rescale GLM-PC matrices via
```
gas_obj.load_rescale(A)
```
  , and infer iso-depths using
```
gas_obj.cal_iso_depth(n_layers)
```
  . Visualise with
```
gas_obj.plot_isodepth(...)
```
  ,
```
gas_obj.plot_clusters_restrict(...)
```
  , and probe continuous/discontinuous gene lists (
```
gas_obj.cont_genes_layer
```
  ).
```
t_gaston.ipynb
```
- For SLAT, construct spatial graphs (
```
Cal_Spatial_Net(adata1, k_cutoff=20)
```
  ), run alignment (
```
run_SLAT(...)
```
  ,
```
spatial_match(...)
```
  ), and examine correspondences through Sankey diagrams (
```
Sankey_multi(...)
```
  ) and lineage-focused subsetting (
```
cal_matching_cell(...)
```
  ).
```
t_slat.ipynb
```

Dependencies

Core:

omicverse

scanpy

anndata

numpy

matplotlib

squidpy

(deconvolution + QC),

networkx

(FlowSig graphs).

Segmentation:
```
cellpose
```
,
```
stardist
```
,
```
opencv-python
```
/
```
tifffile
```
, optional GPU-enabled PyTorch for acceleration.
```
t_cellpose.ipynb
```
Deconvolution:
```
tangram
```
,
```
cell2location
```
,
```
pytorch-lightning
```
,
```
pandas
```
,
```
h5py
```
, plus optional GPU/CUDA stacks; Starfysh additionally needs
```
torch
```
,
```
scikit-learn
```
, and curated signature CSVs.
```
t_decov.ipynb
```
,
```
t_starfysh.ipynb
```
Downstream modelling:
```
scikit-learn
```
(clustering, KMeans, ARI),
```
gseapy==1.0.4
```
for STT enrichment,
```
commot
```
,
```
flowsig
```
,
```
torch
```
-backed modules (STAligner, SpaceFlow, GASTON, SLAT), plus HTML exporters (Plotly) for Sankey plots.

Critical functions and artefacts to surface quickly

Spatial preprocessing:

ov.space.crop_space_visium

ov.space.rotate_space_visium

ov.space.map_spatial_auto

ov.space.map_spatial_manual

ov.space.bin2cell

Deconvolution containers:

ov.space.Deconvolution.preprocess_sc

.preprocess_sp

.deconvolution

.adata_cell2location

.adata_impute

Archetypal/Starfysh:

AA.ArchetypalAnalysis

utils.refine_anchors

utils.run_starfysh

plot_utils.pl_spatial_inf_feature

Clustering/integration:

ov.utils.cluster

ov.space.merge_cluster

ov.space.pySTAligner

ov.space.pySpaceFlow

ov.space.STT

ov.space.GASTON

Cal_Spatial_Net

run_SLAT

Sankey_multi

Communication:

ov.external.commot.pp.ligand_receptor_database

ov.external.commot.tl.spatial_communication

ov.external.flowsig.tl.construct_intercellular_flow_network

Troubleshooting

Coordinate mismatches after rotation/cropping: ensure scalefactors are applied when plotting and cast
```
adata.obsm['spatial']
```
to
```
float64
```
before running
```
map_spatial_auto
```
.
```
t_crop_rotate.ipynb
```
Cellpose runtime errors: verify
```
.btf
```
image paths, memory-map large TIFFs via
```
backend='tifffile'
```
, and adjust
```
mpp
```
plus
```
buffer
```
for dense tissues; GPU runs require matching CUDA/PyTorch builds.
```
t_cellpose.ipynb
```
Gene ID overlap failures in Tangram/cell2location: harmonise identifiers (ENSEMBL vs gene symbols) and drop non-overlapping genes before
```
decov_obj.preprocess_*
```
.
```
t_decov.ipynb
```
mclust errors in spatial clustering: install
```
rpy2
```
and the R
```
mclust
```
package, or switch to the pure Python
```
method='mclust'
```
fallback when R bindings are unavailable.
```
t_cluster_space.ipynb
```
STAligner/SpaceFlow convergence: confirm
```
adata.obsm['spatial']
```
exists and scale coordinates; tune learning rates/regularisation strength when embeddings collapse to a point.
```
t_staligner.ipynb
```
,
```
t_spaceflow.ipynb
```
FlowSig network sparsity: build spatial graphs prior to Moran’s I filtering and raise
```
edge_threshold
```
or increase bootstraps to stabilise edges.
```
t_commot_flowsig.ipynb
```
STT pathway downloads:
```
gseapy
```
lookups need network access; cache gene sets locally and reuse via
```
ov.utils.geneset_prepare(...)
```
to avoid repeated requests.
```
t_stt.ipynb
```
GASTON output directories: provide writable
```
out_dir
```
paths and account for PyTorch nondeterminism when comparing replicate runs.
```
t_gaston.ipynb
```
SLAT alignment quality: regenerate spatial graphs with appropriate
```
k_cutoff
```
and inspect
```
low_quality_index
```
flags before trusting downstream lineage analyses.
```
t_slat.ipynb
```

Examples

"Crop, rotate, and manually re-align Visium coordinates before running Visium HD cell segmentation, then aggregate bins into cell-level AnnData."
"Execute Tangram and cell2location through
```
ov.space.Deconvolution
```
, save trained models, and plot lymph node cell-type proportions."
"Train STAligner and SpaceFlow on DLPFC slices, infer communication networks with COMMOT+FlowSig, and visualise iso-depth layers via GASTON."

References

Tutorials:
```
Tutorials-space/
```

Notebook index:

t_crop_rotate.ipynb

t_cellpose.ipynb

t_cluster_space.ipynb

t_decov.ipynb

t_starfysh.ipynb

t_staligner.ipynb

t_spaceflow.ipynb

t_commot_flowsig.ipynb

t_gaston.ipynb

t_slat.ipynb

t_stt.ipynb

Quick copy/paste commands:
```
reference.md
```