Performs automated quality control and filtering on single-cell RNA-seq data using scverse best practices.
This skill streamlines the preprocessing of single-cell RNA-sequencing data by implementing standardized quality control workflows. It automates the detection of low-quality cells based on count depth, gene detection, and mitochondrial or ribosomal content using Median Absolute Deviation (MAD) thresholds. By following the scverse and scanpy ecosystems' best practices, it generates comprehensive visualizations and filtered datasets, ensuring that downstream analysis—such as clustering and cell-type annotation—is performed on high-quality, reliable data.
Key Features
01147 GitHub stars
02Flexible filtering parameters for species-specific gene patterns (e.g., MT- vs mt-)
03Automated MAD-based outlier detection for counts, genes, and mitochondrial percentages
04Support for .h5ad (AnnData) and 10X Genomics .h5 file formats
05Comprehensive visualization generation including pre- and post-filtering distributions
06Modular Python functions for custom filtering logic and subset-specific QC
Use Cases
01Filtering low-quality cells from a new scRNA-seq experiment before downstream analysis
02Standardizing preprocessing pipelines across multiple datasets using scverse best practices
03Generating standard QC reports and visualizations to assess the quality of a single-cell library
