bio-single-cell-preprocessing

What problem does it solve?

Efficiently perform quality control, filtering, normalization, HVG detection, and scaling for single-cell RNA-seq data using Seurat (R) and Scanpy (Python). This workflow enables data-ready preprocessing for downstream analyses such as clustering, trajectory inference, and differential expression.

Core Features & Use Cases

• QC metric calculation (n_genes, total_counts, percent_mt) and visualization for both Seurat and Scanpy pipelines
• Filter cells and genes with user-defined thresholds to remove low-quality data and potential doublets
• Normalize counts, identify highly variable genes, and scale data to prepare for dimensionality reduction
• Support for both Seurat (R) and Scanpy (Python) workflows, enabling flexible, multi-tool preprocessing

Quick Start

Preprocess my scRNA-seq data by running QC, filtering, normalization, HVG selection, and scaling with Seurat or Scanpy.