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MDhewei
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single-cell-basic-analysis

Analyze single-cell RNA-seq count matrices through QC, normalization, clustering, and marker discovery.

64|12|Updated Apr 7, 2026
Search Tags:#visualization#single-cell#clustering#pca#umap#scrna-seq#marker-genes
One-click install
npx skills add https://github.com/MDhewei/bioinfor-claw --skill single-cell-basic-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-basic-analysis
Source: https://github.com/MDhewei/bioinfor-claw/tree/main/multiomics-data-analysis/single-cell-basic-analysis
Command: npx skills add https://github.com/MDhewei/bioinfor-claw --skill single-cell-basic-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, scipy, and includes scripts (resource) components.

What problem does it solve?

Automates end-to-end single-cell RNA-seq analysis from raw count matrices, including QC filtering, normalization, HVG selection, dimensionality reduction (PCA + UMAP), clustering, and marker gene discovery, using a pure NumPy/Pandas workflow for educational and exploratory use.

Core Features & Use Cases

  • End-to-end preprocessing: QC filtering, normalization, HVG selection, PCA, and clustering.
  • Marker discovery: Wilcoxon-based tests with BH-FDR correction to identify cluster markers.
  • Educational/educational-leaning: avoids heavy dependencies while producing publication-ready outputs for small to medium datasets.

Quick Start

Run the pipeline on a counts matrix by invoking the Python script with your input file and an output directory.

Frequently Asked Questions about single-cell-basic-analysis

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

FAQPage Schema
How do I perform end-to-end scRNA-seq analysis from a raw count matrix?▼

End-to-end scRNA-seq analysis is performed using a pure NumPy and Pandas workflow that processes raw count matrices through QC filtering, normalization, HVG selection, PCA, UMAP, clustering, and marker gene discovery.

Can I run single-cell RNA-seq clustering and UMAP without heavy dependencies like Scanpy or Seurat?▼

Yes, single-cell RNA-seq clustering and UMAP can run using only NumPy, Pandas, SciPy, and Matplotlib, avoiding heavy external dependencies while still generating PCA, UMAP coordinates, and cluster assignments.

What is the best way to identify marker genes for scRNA-seq clusters using Python?▼

Marker genes are identified using Wilcoxon-based tests with BH-FDR correction, producing marker tables that highlight differentially expressed genes across discovered clusters.

Does this NumPy-based scRNA-seq pipeline work for large-scale datasets?▼

This scRNA-seq pipeline is designed for small- to medium-sized datasets for educational and exploratory use, satisfying input validation, orientation detection, and parameter defaults without external dependencies.

How do I normalize scRNA-seq count matrices and select highly variable genes without external packages?▼

ScRNA-seq count matrices are normalized and highly variable genes are selected through built-in preprocessing steps within the pipeline, outputting per-cell metrics and HVG lists using only NumPy and Pandas.

Why does my scRNA-seq analysis require orientation detection for count matrices?▼

Orientation detection automatically validates whether the input count matrix is formatted with genes or cells as rows, ensuring correct preprocessing before QC filtering and normalization are applied.

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