Consequences and opportunities arising due to sparser single-cell RNA-seq datasets

Genome biology, vol. 24, pp. 86

Abstract

With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.

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

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. a.mahfouz@lumc.nl

   Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands. a.mahfouz@lumc.nl

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands. a.mahfouz@lumc.nl

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. m.j.t.reinders@tudelft.nl

   Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands. m.j.t.reinders@tudelft.nl

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands. m.j.t.reinders@tudelft.nl