Analyze single-cell RNA-seq data with a Scanpy pipeline for QC, clustering, and cell-type annotation.
npx skills add https://github.com/OwnLabAI/ownlab --skill scanpy-ownlabai
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/scanpy Command: npx skills add https://github.com/OwnLabAI/ownlab --skill scanpy-ownlabai
An integrated, reproducible pipeline that handles quality control, normalization, dimensionality reduction, clustering, marker gene identification, and visualization for single-cell RNA-seq data.
Run the bundled analysis template on your dataset to obtain a processed AnnData object with cell-type annotations.
High-intent search queries and answers about installing and using this skill.
This scRNA-seq analysis workflow processes FASTQ, 10X, or h5ad inputs through QC, normalization, HVG selection, PCA/UMAP/t-SNE, Leiden clustering, and marker gene identification to produce annotated cell types. It outputs a processed AnnData object with metadata and visualizations.
The standard single-cell RNA-seq pipeline applies QC, normalization, and highly variable gene selection, followed by PCA, neighborhood graph construction, UMAP or t-SNE visualization, and Leiden clustering. It identifies marker genes to annotate discovered cell clusters.
Yes, you can load h5ad files to perform trajectory inference and generate publication-ready visualizations. The workflow supports exploratory analyses including dimensionality reduction plots and marker gene heatmaps directly from the processed AnnData object.
Yes, the single-cell RNA-seq workflow requires both Scanpy and matplotlib as dependencies to compute dimensionality reductions and render visualization plots. These libraries are necessary to execute the bundled analysis template and generate graphical outputs.
After Leiden clustering, identify marker genes by running the differential expression module on the neighborhood graph. The workflow then uses these marker genes to perform cell-type annotation on the scRNA-seq dataset, saving results as metadata within the AnnData object.
Single-cell RNA-seq pipelines require quality control and normalization to filter low-quality cells and correct library size variations before downstream analysis. Without these preparatory steps, dimensionality reduction and clustering produce unreliable results due to technical noise.
StarlitnightlyBuild kb references and quantify scRNA-seq FASTQs into count matrices with OmicVerse alignment wrappers.
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CHENyiru3Analyze scRNA-seq data in R with Seurat for QC, clustering, and marker discovery.
