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

Anne Senabouth,Quan H Nguyen,Stacey B Andersen,Joseph E Powell,Samuel W Lukowski,Xin Mei,Jose Alquicira Hernandez

doi:10.1093/gigascience/giz087

Abstract

BackgroundRecent 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.Findings 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.Conclusions 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.

Highlights

Reviewer Comments to Author: It is good to have alternative workflows for single-cell analysis, and I am glad to see the authors have submitted the package to Bioconductor
It seems to suggest to readers that this is the recommended method, whereas later that is not the case
In these sentences the manuscript claims that this normalization approach is more "robust" without providing any evidence or citation

Summary

Introduction

Title: ascend: R package for analysis of single cell RNA-seq data Reviewer Comments to Author: It is good to have alternative workflows for single-cell analysis, and I am glad to see the authors have submitted the package to Bioconductor. I hope the authors maintain the package and update with new methods as necessary such as if new normalizations or batch corrections are developed. 2. It's still not completely clear to me how the authors extension of the sc-qPCR method is different from MAST.

Results

Conclusion