Does this skill help with quality control (QC)?

Yes, it includes specialized scripts and workflows for calculating QC metrics, filtering mitochondrial content, and removing low-quality cells or genes.

What data formats can I analyze with this skill?

The Scanpy skill supports standard single-cell formats including .h5ad (AnnData), 10X Genomics H5 and MTX files, and general CSV datasets.

Can I generate figures for scientific publications?

Absolutely. The skill includes specific configurations and templates for high-DPI, vector-based (PDF) plots like UMAPs, dot plots, and heatmaps.

How does this handle clustering?

It implements the Leiden community detection algorithm, which is the current best practice for identifying cell populations in single-cell data.

Scanpy Single-Cell Analysis

Name: Scanpy Single-Cell Analysis
Author: Sologa

bySologa

Data Science & ML

Performs comprehensive single-cell RNA-seq analysis workflows including quality control, clustering, and visualization.

About

This skill provides a professional-grade toolkit for single-cell RNA sequencing (scRNA-seq) analysis using the Scanpy framework. It streamlines the entire bioinformatics pipeline—from loading 10X Genomics or AnnData files to performing quality control, normalization, and advanced dimensionality reduction like UMAP and t-SNE. Designed for researchers and bioinformaticians, it includes specialized templates for marker gene identification, cell type annotation, and trajectory inference, ensuring reproducible and publication-quality results for transcriptomic studies.

Key Features

Advanced trajectory inference and pseudotime analysis using PAGA
Standardized QC pipelines for filtering mitochondrial genes and low-quality cells
Comprehensive dimensionality reduction including PCA, UMAP, and t-SNE
Publication-ready visualization tools for heatmaps, dot plots, and violin plots
0 GitHub stars
Automated marker gene identification and cluster-based cell type annotation

Use Cases

Comparing differential gene expression between experimental conditions across cell types
Processing raw 10X Genomics data into annotated cell clusters and visualizations
Automating quality control and normalization for large-scale transcriptomic datasets

About

Key Features

Advanced trajectory inference and pseudotime analysis using PAGA
Standardized QC pipelines for filtering mitochondrial genes and low-quality cells
Comprehensive dimensionality reduction including PCA, UMAP, and t-SNE
Publication-ready visualization tools for heatmaps, dot plots, and violin plots
0 GitHub stars
Automated marker gene identification and cluster-based cell type annotation

Use Cases

Comparing differential gene expression between experimental conditions across cell types
Processing raw 10X Genomics data into annotated cell clusters and visualizations
Automating quality control and normalization for large-scale transcriptomic datasets