single-cell-rna-qc

MAD-based quality control for single-cell RNA-seq data identifies and filters low-quality cells by applying Median Absolute Deviation thresholds to total counts, detected genes, and mitochondrial content, producing clean QC-annotated datasets.

MAD filtering for scRNA-seq identifies outlier cells based on deviations from the median across QC metrics like total counts and mitochondrial content, removing technical artifacts while retaining biologically viable cells for downstream analysis.

Yes, this Skill uses scanpy and anndata to automate single-cell RNA-seq quality control, calculating standardized metrics and generating filtered .h5ad outputs and QC visualizations without requiring manual threshold tuning.

Yes, the single-cell QC workflow handles per-sample variations by applying MAD-based thresholds independently, ensuring accurate outlier detection across heterogeneous samples within a single AnnData object.

Single-cell RNA-seq quality control generates standard QC plots visualizing total counts, detected genes, and mitochondrial content distributions, helping verify filtering thresholds and assess dataset quality before downstream analysis.

Filtering cells by mitochondrial content in scRNA-seq is best handled by applying MAD-based thresholds alongside hard cutoffs, automatically identifying high-mitochondria cells indicative of cell damage while preserving healthy populations.