single-cell-rna-qc

To perform single-cell RNA-seq quality control with scanpy, this Skill automates the process by applying scverse best practices, including MAD-based filtering and visualization, directly on your input data.

MAD-based filtering in single-cell RNA-seq quality control uses Median Absolute Deviation to robustly detect and filter out low-quality cell outliers, ensuring data integrity for downstream analysis.

Yes, you can use anndata files for single-cell RNA-seq quality control. The Skill processes standard .h5ad input files and requires anndata, scanpy, scipy, matplotlib, and seaborn to execute the workflow.

Yes, the single-cell RNA-seq QC workflow generates comprehensive visualizations for data quality assessment, utilizing matplotlib and seaborn to provide detailed plots that help evaluate filtered results.

You do not need loompy to run scverse best practices for scRNA-seq data, as it is only an optional dependency for advanced processing. The core workflow requires anndata, scanpy, numpy, and scipy.

The best way to automate scRNA-seq quality control is using a Skill that implements scverse best practices, applying MAD-based filtering and generating visualizations to identify low-quality cells efficiently.