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数据与复现单细胞与空间组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
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bio-single-cell-data-io

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

Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.

OpenClawNanoClaw分析处理复现实验bio-single-cell-data-io🧬 bioinformatics (gptomics bio-* suite)bioinformatics — single-cell & spatial omicsread

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-data-io

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

  • Read，write，、 create single-cell data objects ，用于 analysis。
  • adata = sc.read_10x_mtx('filtered_feature_bc_matrix/'，var_names='gene_symbols'，cache=True) print(f'Loaded {adata.n_obs} cells x {adata.n_vars} genes')。

原始文档

SKILL.md 摘录

Scanpy (Python)

Goal: Load, create, and save single-cell data objects using Scanpy and AnnData.

Approach: Read 10X Genomics output, CSV, or Loom formats into AnnData objects, manipulate metadata and layers, and write to h5ad format.

"Load my 10X data" → Read Cell Ranger output directory or h5 file into an AnnData object with expression matrix, cell barcodes, and gene annotations.

Read 10X h5 file directly

adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')


## - adata.layers: alternative matrices (raw counts, normalized)

print(f'Shape: {adata.shape}')
print(f'Cell metadata: {adata.obs.columns.tolist()}')
print(f'Gene metadata: {adata.var.columns.tolist()}')

适用场景

  • 可用于loading 10X Genomics data，importing/exporting h5ad 、 RDS files，creating Seurat objects 、 AnnData objects，、 converting between formats。
  • 适合在loading，saving，或 converting single-cell data formats时使用。

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

  • preprocessing - QC filtering and normalization after loading
  • clustering - Dimensionality reduction and clustering
  • markers-annotation - Find marker genes and annotate cell types

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