ascend: R package for analysis of single-cell RNA-seq data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) experiments are generating unprecedented amounts of data.
• Analyzing scRNA-seq data requires specialized statistical methods and bioinformatics tools.
• Existing analysis packages often lack robustness, speed, or flexibility for integrated workflows.

Purpose of the Study:

• To introduce ascend, an R package designed for streamlined preliminary analysis of scRNA-seq data.
• To provide a robust, computationally efficient, and flexible framework for scRNA-seq data analysis.
• To facilitate integration with other bioinformatics tools and R functions.

Main Methods:

• The ascend package offers a comprehensive suite of tools for scRNA-seq data preprocessing and analysis.
• Includes functions for cell and gene filtering, quality control, normalization, and dimension reduction.
• Incorporates methods for clustering, differential expression analysis, and diverse visualization techniques.

Main Results:

• ascend provides fast parallel computation and efficient memory management for large datasets.
• The package supports data from any high-throughput scRNA-seq platform.
• Functions for data object conversion enable interoperability between different software packages.

Conclusions:

• ascend simplifies complex scRNA-seq data analysis, making it accessible to users with varying programming experience.
• The package addresses key statistical challenges inherent in scRNA-seq data analysis.
• ascend offers a flexible and efficient solution for preliminary scRNA-seq data analysis workflows.