Work

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

Year: 2021

Type: preprint

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 .

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/2021.02.01.429187

Authors: Gerard A. Bouland, Ahmed Mahfouz, Marcel J.T. Reinders

Institutions: Leiden University Medical Center, Delft University of Technology

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Award: BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology