Data & ReproSingle-Cell & Spatial OmicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction
SP

spatial-deconvolution

Maintainer FreedomIntelligence · Last updated April 1, 2026

Deconvolution estimates cell type proportions in each spatial spot using a reference single-cell dataset. Essential for Visium data where spots contain multiple cells.

OpenClawNanoClawAnalysisReproductionspatial-deconvolutionopenclaw medical skillsadditional scientific toolsspatial

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

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

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Estimate cell type composition in spatial spots using scRNA-seq references.
  • Deconvolution estimates cell type proportions in each spatial spot using a reference single-cell dataset. Essential for Visium data where spots contain multiple cells.

Source Doc

Excerpt From SKILL.md

Using cell2location

import cell2location
from cell2location.utils.filtering import filter_genes
from cell2location.models import RegressionModel

## Load reference scRNA-seq

adata_ref = sc.read_h5ad('reference_scrna.h5ad')
adata_ref.obs['cell_type'] = adata_ref.obs['cell_type'].astype('category')

## Load spatial data

adata_vis = sc.read_h5ad('spatial_data.h5ad')

Use cases

  • Use spatial-deconvolution for single-cell or spatial omics analysis.
  • Apply spatial-deconvolution to clustering, integration, or trajectory workflows.

Not for

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

Upstream Related Skills

  • spatial-data-io - Load spatial data
  • single-cell/data-io - Load scRNA-seq reference
  • spatial-visualization - Visualize deconvolution results
  • single-cell/markers-annotation - Annotate reference cell types

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