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

  1. Anne Senabouth
  2. Samuel W Lukowski
  3. Jose Alquicira Hernandez
  4. Stacey B Andersen
  5. Xin Mei
  6. Quan H Nguyen
  7. Joseph E Powell

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Abstract

Background

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.

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.

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  1. GigaScience

    Abstract

    **Reviewer 2. Rhonda Bacher ** 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. I only have two comments that I hope the authors try to clarify further:

    1. The statement starting with "Optionally, after batch-to-batch normalisation, we also..." should not be in that location. It seems to suggest to readers that this is the recommended method, whereas later that is not the case. In these sentences the manuscript also claims that this normalization approach is more "robust" without providing any evidence or citation.
    2. It's still not completely clear to me how the authors extension of the sc-qPCR method is different from MAST. The same authors of the qPCR method extended it here: "MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data". MAST is also an LRT, but I am assuming that here you are not using the detection rate as a covariate? That's OK if true, it just needs to be clear to the reader. I imagine this could be a frequently asked question by users down the road, so even a sentence on how it is different from (or similar to) MAST would help. Suggestion only: I may have missed it, but it might be helpful to include a statement that says something like "Statistical methods for single-cell analysis are constantly evolving. Here we have implemented XX. The flexibility of ascend allows it to adapt as future methods are developed and prove useful".
  2. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giz087

    Anne Senabouth 1Institute for Molecular Bioscience, University of Queensland, Brisbane, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteSamuel W Lukowski 1Institute for Molecular Bioscience, University of Queensland, Brisbane, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteJose Alquicira Hernandez 1Institute for Molecular Bioscience, University of Queensland, Brisbane, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteStacey Andersen 1Institute for Molecular Bioscience, University of Queensland, Brisbane, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteXin Mei 2South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, ChinaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteQuan H Nguyen 1Institute for Molecular Bioscience, University of Queensland, Brisbane, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteJoseph E Powell 1Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia3Queensland Brain Institute, University of Queensland, Brisbane, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this site

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giz087 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101872

  3. Version published to 10.1093/gigascience/giz087
  4. Version published to 10.1101/207704v1 on bioRxiv