bio-workflows-scrnaseq-pipeline
End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scrnaseq-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: Cell Ranger 8.0+, ggplot2 3.5+, numpy 1.26+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Single-Cell RNA-seq Pipeline **"Analyze my single-cell RNA-seq data from counts to cell types"** → Orchestrate QC filtering, normalization (scanpy/Seurat), batch integration (scVI/Harmony), clustering, marker detection, cell type annotation, and trajectory inference. Complete workflow from 10X Genomics Cell Ranger output to annotated cell types. ## Workflow Overview ``` 10X data (filtered_feature_bc_matrix) | v [1. Load Data] ---------> Read10X / read_10x_h5 | v [2. QC Filtering] ------> nFeature, percent.mt, doublets | v [3. Normalization] -----> SCTransform or LogNormalize | v [4. HVG Selection] -----> FindVariableFeatures | v [5. Dim Reduction] -----> PCA → UMAP | v [6. Cluster
- Version Compatibility
- Workflow Overview
- Primary Path: Seurat (R)
- Step 1: Load 10X Data
- Step 2: Quality Control
- Step 3: Doublet Detection
- Step 4: Normalization with SCTransform
- Step 5: Dimensionality Reduction
- Step 6: Clustering
- Step 7: Find Marker Genes
- Step 8: Cell Type Annotation
- Alternative Path: Scanpy (Python)
- Parameter Recommendations
- Troubleshooting
What does the bio-workflows-scrnaseq-pipeline skill do?
End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scrnaseq-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.