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

Clicks: 248
ID: 53007
2019
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Recent developments in single-cell RNA sequencing (scRNA-seq) platforms have vastly increased the number of cells typically assayed in an experiment. Analysis of scRNA-seq data is multidisciplinary in nature, requiring careful consideration of the application of statistical methods with respect to the underlying biology. Few analysis packages exist that are at once robust, are computationally fast, and allow flexible integration with other bioinformatics tools and methods.ascend is an R package comprising tools designed to simplify and streamline the preliminary analysis of scRNA-seq data, while addressing the statistical challenges of scRNA-seq analysis and enabling flexible integration with genomics packages and native R functions, including fast parallel computation and efficient memory management. The package incorporates both novel and established methods to provide a framework to perform cell and gene filtering, quality control, normalization, dimension reduction, clustering, differential expression, and a wide range of visualization functions.ascend is designed to work with scRNA-seq data generated by any high-throughput platform and includes functions to convert data objects between software packages. The ascend workflow is simple and interactive, as well as suitable for implementation by a broad range of users, including those with little programming experience.
Reference Key
senabouth2019ascendgigascience Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Senabouth, Anne;Lukowski, Samuel W;Hernandez, Jose Alquicira;Andersen, Stacey B;Mei, Xin;Nguyen, Quan H;Powell, Joseph E;
Journal gigascience
Year 2019
DOI giz087
URL
Keywords
single cell clustering scrna-seq data visualization normalization r package differential expression filtering

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.