kwp-bio-research-single-cell-rna-qc

Automated quality control for single-cell RNA-seq calculates metrics and filters cells using MAD-based outlier detection. It removes low-quality cells with high mitochondrial content or low gene detection rates to ensure downstream analysis uses reliable biological signals.

MAD-based outlier detection in scRNA-seq quality control identifies anomalous cells using median absolute deviations from standard scverse best practices. It automatically flags cells deviating significantly from typical metric distributions across diverse tissue types.

Yes, this workflow uses scanpy to process .h5ad and .h5 files, automatically filtering cells with high mitochondrial content. It generates comprehensive QC visualizations to help validate the filtering thresholds applied to your dataset.

You can apply custom filtering thresholds to specific cell types or tissue subsets using the modular analysis capabilities. This provides granular control over the scverse-compliant workflow, allowing distinct MAD thresholds for different cellular populations within your dataset.

Yes, you need anndata, scanpy, scipy, matplotlib, seaborn, and numpy installed. These dependencies are required for computational processing, MAD-based metric calculations, and generating comprehensive quality control visualizations for your single-cell datasets.