single-cell-rna-qc

Automated quality control on single-cell RNA-seq data calculates QC metrics, applies MAD-based filtering for outlier detection, removes low-quality cells and genes, and generates visualizations. This process uses scverse best practices to prepare datasets for downstream analysis.

Yes, you can use scanpy to filter low-quality cells in .h5ad files. The automated pipeline supports both .h5ad and .h5 file formats, calculating QC metrics and applying MAD-based filtering to identify and remove outliers.

MAD-based filtering for single-cell RNA-seq outlier detection is a statistical method using the median absolute deviation to identify low-quality cells. It automatically flags data points that deviate significantly from typical values, ensuring robust dataset cleaning.

Yes, the single-cell RNA-seq QC pipeline supports custom analysis approaches. It provides a complete pipeline script for standard automated workflows, alongside modular functions that allow you to build custom quality control logic using scanpy and anndata.

Visualizations generated during single-cell RNA-seq data preprocessing include comprehensive plots created with matplotlib and seaborn. These visual outputs help assess cell quality, evaluate filtering thresholds, and verify the removal of low-quality cells and genes.

You need to remove low-quality cells before single-cell RNA-seq analysis to ensure high-quality datasets for downstream processing. Low-quality cells introduce noise and technical artifacts that can skew biological interpretations and compromise analytical results.