数据与复现单细胞与空间组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-single-cell-preprocessing

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

Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.

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

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

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

Python：scanpy.pp.filter_cells() → normalize_total() → log1p() → highly_variable_genes()。
R：Seurat：：NormalizeData() → FindVariableFeatures() → ScaleData()。
Preprocess my scRNA-seq data" → Filter low-quality cells/genes，normalize counts，identify highly variable genes，、 prepare data ，用于 dimensionality reduction 、聚类. Python：scanpy.pp.filter_cells() → normalize_total() → log1p() → highly_variable_genes() R：Seurat：：NormalizeData() → FindVariableFeatures() → ScaleData()。
Quality control，filtering，normalization，、 feature selection ，用于 scRNA-seq data。
adata.var['mt'] = adata.var_names.str.startswith('MT-') sc.pp.calculate_qc_metrics(adata，qc_vars=['mt']，percent_top=None，log1p=False，inplace=True)。

原始文档

SKILL.md 摘录

Scanpy (Python)

Goal: Preprocess scRNA-seq data through QC filtering, normalization, and feature selection using Scanpy.

Approach: Calculate per-cell quality metrics, filter low-quality cells/genes, normalize library sizes, identify highly variable genes, and scale for downstream analysis.

Filter cells by QC metrics

sc.pp.filter_cells(adata, min_genes=200) sc.pp.filter_cells(adata, max_genes=5000)

Filter by mitochondrial percentage

adata = adata[adata.obs['pct_counts_mt'] < 20, :].copy()

适用场景

可用于calculating QC metrics，filtering cells 、 genes，normalizing counts，identifying highly variable genes，、 scaling data。
适合在filtering，normalizing，、 selecting features in single-cell data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

data-io - Load data before preprocessing
clustering - PCA and clustering after preprocessing
markers-annotation - Find markers after clustering

AnnData

AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observ…

Claude CodeOpenClaw分析处理

K-Dense-AI/claude-scientific-skills查看

数据与复现单细胞与空间组学

Arboreto

Arboreto is a computational library for inferring gene regulatory networks (GRNs) from gene expression data using parall…

Claude CodeOpenClaw分析处理

K-Dense-AI/claude-scientific-skills查看

数据与复现单细胞与空间组学

bio-imaging-mass-cytometry-cell-segmentation

Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for n…

OpenClawNanoClaw分析处理

FreedomIntelligence/OpenClaw-Medical-Skills查看

数据与复现单细胞与空间组学

bio-read-qc-umi-processing

Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep…

OpenClawNanoClaw分析处理

FreedomIntelligence/OpenClaw-Medical-Skills查看