AN
Data & Repro
AnnData
AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observation metadata (obs)…
K-Dense-AI/claude-scientific-skillsView
Data & Repro
BI
bio-workflows-scrnaseq-pipeline
Maintainer FreedomIntelligence · Last updated April 1, 2026
Single-cell RNA-seq end-to-end: Cell Ranger → Scanpy → clustering.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflows-scrnaseq-pipeline
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Complete workflow from 10X Genomics Cell Ranger output to annotated cell types.
- data_dir <- 'cellranger_output/filtered_feature_bc_matrix' counts <- Read10X(data.dir = data_dir).
Source Doc
Excerpt From SKILL.md
Step 1: Load 10X Data
library(Seurat)
library(ggplot2)
library(dplyr)
## Create Seurat object
seurat_obj <- CreateSeuratObject(counts = counts, project = 'my_project',
min.cells = 3, min.features = 200)
Calculate QC metrics
seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-') seurat_obj[['percent.ribo']] <- PercentageFeatureSet(seurat_obj, pattern = '^RP[SL]')
Use cases
- Use bio-workflows-scrnaseq-pipeline for single-cell or spatial omics analysis.
- Apply bio-workflows-scrnaseq-pipeline to clustering, integration, or trajectory workflows.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- single-cell/data-io - Loading different formats
- single-cell/preprocessing - QC details
- single-cell/doublet-detection - Doublet methods comparison
- single-cell/clustering - Clustering parameters
- single-cell/markers-annotation - Annotation strategies
- single-cell/multimodal-integration - CITE-seq, multiome
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