SCI Skills

EN Index
Data & ReproGIS & Remote SensingFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction
SP

spatial-transcriptomics-tutorials-with-omicverse

Maintainer FreedomIntelligence · Last updated March 31, 2026

Use this skill to navigate the spatial analysis tutorials located under [`Tutorials-space`](../../omicverse_guide/docs/Tutorials-space/). The notebooks span preprocessing utilities ([`t_crop_rotate.ipynb`](../../omicverse_guide/docs/Tutorials-space/t_crop_rotate.ipynb), [`t_cellpose.ipynb`](../../omicverse_guide/docs/Tutorials-space/t_cellpose.ipynb)), deconvolution frameworks ([`t_decov.ipynb`](../../omicverse_guid….

OpenClawNanoClawAnalysisReproductionspatial-tutorials🔬 omics & computational biologysingle-cell & spatial omicsguide

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/spatial-tutorials

Maintainer
FreedomIntelligence
License
MIT
Last updated
March 31, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • 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.
  • 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.

Source Doc

Excerpt From SKILL.md

Preprocess

  1. Load spatial slides and manipulate coordinates
    • Import omicverse as ov, scanpy as sc, and enable plotting defaults with ov.plot_set() or 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').
  2. 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').
  3. 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

Use cases

  • Use spatial-transcriptomics-tutorials-with-omicverse for GIS and remote-sensing workflows.
  • Apply spatial-transcriptomics-tutorials-with-omicverse to earth observation and spatial analysis tasks.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Related skills

Related skills

Back to directory
BI
Data & ReproGIS & Remote Sensing

bio-imaging-mass-cytometry-spatial-analysis

Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Us…

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & ReproGIS & Remote Sensing

bio-workflows-imc-pipeline

Imaging mass cytometry: segmentation → phenotyping → spatial analysis.

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
HE
Data & ReproGIS & Remote Sensing

hedgefundmonitor

Free, open REST API from the U.S. Office of Financial Research (OFR) providing aggregated hedge fund time series data. No API key or registr…

Claude CodeAnalysis
K-Dense-AI/claude-scientific-skillsView
HY
Data & ReproGIS & Remote Sensing

HypoGeniC

Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework…

Claude CodeOpenClawAnalysis
K-Dense-AI/claude-scientific-skillsView