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spatial-deconvolution

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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

OpenClawNanoClaw分析处理复现实验spatial-deconvolutionopenclaw medical skillsadditional scientific toolsspatial

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者
FreedomIntelligence
许可
MIT
最近更新
2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

  • Estimate cell type composition in spatial spots ，使用 scRNA-seq references。
  • Deconvolution estimates cell type proportions in each spatial spot ，使用 reference single-cell 数据集. Essential ，用于 Visium data where spots contain multiple cells。

原始文档

SKILL.md 摘录

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')

适用场景

  • Use spatial-deconvolution ，用于 single-cell 或 spatial omics analysis。
  • Apply spatial-deconvolution to 聚类，integration，或 trajectory workflows。

不适用场景

  • Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。

上游相关技能

  • 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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