Automate single-cell RNA-seq analysis with scanpy from QC to visualization.
npx skills add https://github.com/jonnabio/ace-framework --skill scanpy-jonnabio
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/scientific/scanpy Command: npx skills add https://github.com/jonnabio/ace-framework --skill scanpy-jonnabio
Simplifies and accelerates the complex process of single-cell RNA-seq data analysis, providing a standardized, automated workflow for quality control, visualization, and interpretation.
Import the Scanpy skill and analyze your data with the following command:
import scanpy as sc
sc.read_10x_h5('path/to/data/')
sc.pp.calculate_qc_metrics()
sc.pp.filter_cells(min_genes=200)
sc.pp.scale()
sc.tl.pca()
sc.tl.umap()
sc.tl.leiden()
High-intent search queries and answers about installing and using this skill.
Automating single-cell RNA-seq analysis involves using a pipeline that handles data loading, quality control, normalization, dimensionality reduction like UMAP, and Leiden clustering to identify cell types.
Quality control and normalization for single-cell data are best handled by automatically filtering low-quality cells and genes, removing background noise, and scaling data to standardize it across samples for downstream analysis.
Yes, single-cell RNA-seq analysis using scanpy is compatible with various file formats including h5ad, mtx, and CSV, allowing you to load biological research data directly into the analysis pipeline.
Single-cell dimensionality reduction uses PCA, UMAP, and t-SNE to visualize high-dimensional data, while marker gene identification applies differential expression analysis to help annotate cell types within clusters.
Yes, single-cell analysis pipelines using scanpy require dependencies like pandas, numpy, and anndata to handle data structures and perform the underlying numerical computations for biological research.
StarlitnightlyOrchestrate RNA-seq analysis from FASTQ files to gene count matrices.
TianGzlabAutomate cell type annotation for single-cell omics data using marker genes, CellTypist, SingleR, or scmap.
MubasherMohammedGenerate visualizations and reports for bulk and single-cell RNA-seq differential expression.
galeepAutomates scRNA-seq analysis including QC, normalization, clustering, and visualization with AnnData and scanpy.