GTestimate: improving relative gene expression estimation in scRNA-seq using the Good–Turing estimator
By choosing a more suitable estimator for the relative gene expression per cell, we were able to improve scRNA-seq normalization, with potentially large implications for downstream results. GTestimate is available as an easy-to-use R-package and compatible with a variety of workflows, which should enable widespread adoption.
Authors
- Juergen A. Knoblich (ORCID: https://orcid.org/0000-0002-6751-3404)
- Martin Fahrenberger (ORCID: https://orcid.org/0000-0003-1107-0197)
- Christopher Esk (ORCID: https://orcid.org/0000-0003-1833-0801)
- Arndt von Haeseler (ORCID: https://orcid.org/0000-0002-3366-4458)
Institutions
- Institute of Molecular Biotechnology (AT)
- Universität Innsbruck (AT)
- Max Perutz Labs (AT)
- Ludwig Boltzmann Institute for Age Research (AT)
- Ludwig Boltzmann Institute Applied Diagnostics (AT)
- Vienna Biocenter (AT)
- Ludwig Boltzmann Institute for Cancer Research (AT)
- Medical University of Vienna (AT)
Publication Details
- Journal
- GigaScience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/gigascience/giaf084
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81