Differential dropout analysis captures biological variation in single-cell RNA sequencing data

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose differential dropout analysis (DDA) to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that DDA accurately detects biological variation and can assess the relative abundance of transcripts more robustly than methods relying on counts. DDA is available at https://github.com/gbouland/DDA .

Authors 3

1. Gerard A. Bouland Aachen Department of Human Genetics

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 333ZC, The Netherlands

2. Ahmed Essam Mahfouz corresponding Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2ZC, The Netherlands

3. Marcel J. T. Reinders corresponding Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2ZC, The Netherlands