scater

scater facilitates rigorous pre-processing, quality control, normalization and visualization of single-cell RNA-seq gene expression data in R.

Key Features:

• Quality Control: scater provides tools for quality control of single-cell RNA-seq data, addressing biases, artifacts and other sources of unwanted variation.
• Normalization: scater includes normalization methods to correct for unwanted variation in single-cell RNA-seq data.
• Visualization: scater provides a rich suite of plotting tools specifically for single-cell data.
• Flexible Data Structure: scater uses a flexible data structure that is compatible with existing tools and can serve as infrastructure for future software development.
• Workflow Integration: scater provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis.

Scientific Applications:

• Single-Cell RNA-seq Analysis: scater supports gene expression analysis at the level of individual cells using single-cell RNA sequencing data.
• Pre-processing Pipeline: scater converts raw sequence data into analysis-ready expression datasets for downstream computational and biological analysis.

Methodology:

scater processes raw sequencing reads through pre-processing, quality control and normalization steps to produce a high-quality expression dataset.