Data Denoising and Post-Denoising Corrections in Single Cell RNA Sequencing

• Divyansh Agarwal ^[1] ; Jingshu Wang ^[2] ; Zhang, Nancy R. ^[1]
  1. [1] University of Pennsylvania

     University of Pennsylvania

     City of Philadelphia, Estados Unidos

     
  2. [2] University of Chicago

     University of Chicago

     City of Chicago, Estados Unidos

     
• Localización: Statistical science, ISSN 0883-4237, Vol. 35, Nº. 1, 2020 (Ejemplar dedicado a: Statistics and Science), págs. 112-128
• Idioma: inglés
• DOI: 10.1214/19-sts7560
• Texto completo no disponible (Saber más ...)
• Resumen

  • Single cell sequencing technologies are transforming biomedical research. However, due to the inherent nature of the data, single cell RNA sequencing analysis poses new computational and statistical challenges. We begin with a survey of a selection of topics in this field, with a gentle introduction to the biology and a more detailed exploration of the technical noise. We consider in detail the problem of single cell data denoising, sometimes referred to as “imputation” in the relevant literature. We discuss why this is not a typical statistical imputation problem, and review current approaches to this problem. We then explore why the use of denoised values in downstream analyses invites novel statistical insights, and how denoising uncertainty should be accounted for to yield valid statistical inference.

    The utilization of denoised or imputed matrices in statistical inference is not unique to single cell genomics, and arises in many other fields. We describe the challenges in this type of analysis, discuss some preliminary solutions, and highlight unresolved issues.