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bioinformatics-init-analysis

Automates the 7-step initial analysis pipeline for high-dimensional single-cell data, generating HTML reports.

Updated Apr 1, 2026
Search Tags:#bioinformatics#pipeline#data-analysis#single-cell#scRNA-seq#report-generation#cytof
One-click install
npx skills add https://github.com/xiaoyu-ops/the_work_of_dedup --skill bioinformatics-init-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bioinformatics-init-analysis
Source: https://github.com/xiaoyu-ops/the_work_of_dedup/tree/main/.claude/skills/bioinformatics-init-analysis
Command: npx skills add https://github.com/xiaoyu-ops/the_work_of_dedup --skill bioinformatics-init-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scanpy, anndata, matplotlib, seaborn, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates the end-to-end initial data analysis workflow for high-dimensional single-cell biology data, reducing manual setup and speeding up insights.

Core Features & Use Cases

  • 7-step pipeline: Load, QC, Normalize, Dimensionality reduction (PCA/UMAP), Clustering, Marker Analysis, and Report generation.
  • Auto-detection: Automatically identifies data type (CyTOF, scRNA-seq, flow cytometry) and applies appropriate processing.
  • Modular workflow: Use the full pipeline or import individual steps for custom analyses.
  • Clinical/technical reporting: Generates HTML reports with plain-language interpretations and visualizations.
  • Scalability: Subsampling support and parallelizable stages for large datasets.

Quick Start

Run the 7-step pipeline on your dataset using the provided entry point.

Frequently Asked Questions about bioinformatics-init-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate scRNA-seq data analysis from raw counts to clustering?▼

Automate scRNA-seq data analysis using a 7-step pipeline that handles loading, QC, normalization, dimensionality reduction, clustering, and marker analysis to generate an HTML report.

What is the best way to run initial CyTOF data analysis without manual setup?▼

Run initial CyTOF data analysis through an automated pipeline with data-type auto-detection, applying appropriate QC, normalization, dimensionality reduction, and clustering steps modularly.

Does the single-cell analysis pipeline support flow cytometry data?▼

Yes, the single-cell analysis pipeline supports flow cytometry data through auto-detection, applying standardized processing steps from loading to marker analysis and report generation.

Can I use scanpy and anndata for single-cell clustering in this workflow?▼

Yes, you can use scanpy and anndata for single-cell clustering, as the pipeline leverages these dependencies to execute dimensionality reduction and clustering within its modular workflow.

How do I handle large scRNA-seq datasets for dimensionality reduction?▼

Handle large scRNA-seq datasets for dimensionality reduction using built-in subsampling support and parallelizable stages to scale the analysis workflow efficiently.

Why generate an HTML report for single-cell data analysis?▼

Generate an HTML report for single-cell data analysis to obtain clinical and technical interpretations with plain-language summaries and visualizations directly from the automated pipeline output.

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