Assessing the Influence of scRNA-Seq Data Normalization on Dimensionality Reduction Outcomes

Marcel Ochocki, Michal Marczyk, Michal Marczyk, Joanna Zyla

2025

Abstract

Through the decades, improvements in high-throughput molecular biology techniques have brought to the level of sequencing transcripts from single cells (scRNA-Seq) instead of bulk material. Implementing these new techniques requires innovative analytical methods and knowledge about their performance. Data normalization is a crucial step in the bioinformatical pipeline applied in scRNA-Seq analysis. We evaluated the impact of six commonly used normalization methods on two dimensionality reduction methods, namely tSNE and UMAP, using three real scRNA-Seq datasets. We tested dispersion and clustering efficiency using three clustering algorithms after dimensionality reduction. Our results demonstrated that simple normalization methods, such as log2 or Freeman-Tukey, as well as scran normalization consistently outperformed other scRNA-seq-dedicated techniques, yielding superior dimensionality reduction and clustering efficiency for small and medium-sized datasets. Regardless of no statistically significant enhancement in results for any dimensionality reduction methods or clustering techniques, the Louvain clustering method consistently demonstrated lower performance results. We conclude, that the choice of normalization technique should be carefully tailored to the dataset’s size and characteristics since it may affect the final within-pipeline processing results.

Paper Citation

in Harvard Style

Ochocki M., Marczyk M. and Zyla J. (2025). Assessing the Influence of scRNA-Seq Data Normalization on Dimensionality Reduction Outcomes. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-731-3, SciTePress, pages 504-515. DOI: 10.5220/0013318700003911

in Bibtex Style

@conference{bioinformatics25,
author={Marcel Ochocki and Michal Marczyk and Joanna Zyla},
title={Assessing the Influence of scRNA-Seq Data Normalization on Dimensionality Reduction Outcomes},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2025},
pages={504-515},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013318700003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Assessing the Influence of scRNA-Seq Data Normalization on Dimensionality Reduction Outcomes
SN - 978-989-758-731-3
AU - Ochocki M.
AU - Marczyk M.
AU - Zyla J.
PY - 2025
SP - 504
EP - 515
DO - 10.5220/0013318700003911
PB - SciTePress