Zum Hauptinhalt springen
Dieser Inhalt ist noch nicht in Ihrer Sprache verfügbar und wird auf Englisch angezeigt.

Alterlab Scanpy

Skill Aktiv

Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata. Part of the AlterLab Academic Skills suite.

Zweck

To enable researchers to perform standard exploratory analysis on single-cell RNA-seq data by providing a robust, well-documented workflow powered by the Scanpy toolkit.

Funktionen

  • Standard single-cell RNA-seq analysis workflow
  • Quality control and filtering
  • Normalization and feature selection
  • Dimensionality reduction (PCA, UMAP, t-SNE)
  • Clustering and marker gene identification
  • Cell type annotation guidance
  • Publication-quality plot generation
  • Trajectory inference and differential expression support

Anwendungsfälle

  • Performing exploratory data analysis on new scRNA-seq datasets
  • Identifying cell populations and marker genes
  • Generating figures for scientific publications or presentations
  • Benchmarking or validating scRNA-seq analysis pipelines

Nicht-Ziele

  • Deep learning models for scRNA-seq (use scvi-tools)
  • Data format questions (use anndata documentation)
  • Advanced omics integration beyond single-cell RNA-seq
  • Real-time analysis of large-scale genomic data streams

Praktiken

  • Reproducible research
  • Single-cell analysis best practices
  • Data visualization standards

Voraussetzungen

  • Python 3 environment
  • Scanpy and its dependencies (pandas, numpy, matplotlib, etc.)
  • Input data in a compatible format (.h5ad, 10X, CSV)

Trust

  • warning:Issues AttentionThere are 2 open issues and 0 closed issues in the last 90 days, indicating slow maintenance engagement.

Code Execution

  • info:LoggingThe script provides console output for progress and QC metrics, but does not implement a dedicated local audit log file for actions.

Execution

  • info:Pinned dependenciesThe template script lists imports, but specific pinned versions or lockfiles for dependencies are not explicitly provided within the repository.

Installation

npx skills add AlterLab-IEU/AlterLab-Academic-Skills

Führt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.

Qualitätspunktzahl

96 /100
Analysiert 1 day ago

Vertrauenssignale

Letzter Commit17 days ago
Sterne15
LizenzMIT
Status
Quellcode ansehen

Ähnliche Erweiterungen

Scanpy

99

Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.

Skill
K-Dense-AI

PyDESeq2

100

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Skill
K-Dense-AI

Gtars

99

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Skill
K-Dense-AI

AnnData

99

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

Skill
K-Dense-AI

Fit Drift Diffusion Model

100

Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.

Skill
pjt222

Pysam

99

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Skill
K-Dense-AI