# Towards reproducible single-cell transcriptomics analysis

Audrey Onfroy, Piotr Topilko, Sophie Hüe, Denis Thieffry

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Audrey Onfroy, Piotr Topilko, Sophie Hüe, Denis Thieffry. Towards reproducible single-cell transcriptomics analysis. JOBIM 2025, Jul 2025, Bordeaux, France. 2025. hal-05134733

HAL Id: hal-05134733

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Submitted on 28 Jun 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Towards reproducible single-cell transcriptomics analysis

Audrey ONFROY¹, Piotr TOPILKO¹, Sophie HÜE², Denis THIEFFRY³,⁴

1) Mondor Institute for Biomedical Research, U955 Inserm, Université Paris-Est Créteil, Team 9, 94000, Créteil, France ;
2) Mondor Institute for Biomedical Research, U955 Inserm, Université Paris-Est Créteil, Team 16, 94000, Créteil, France ;
3) École Normale Supérieure, Department of Biology, PSL University, 75005, Paris, France ; 4) Curie Institute, INSERM U1331, Mines Paris, PSL University, 75005, Paris, France ; contact: audrey.onfroy@inserm.fr

## SINGLE-CELL TRANSCRIPTOMICS & REPRODUCIBILITY

Single-cell RNA sequencing (scRNA-Seq) enables the profiling of **individual cells** from a biological sample. The outcome of scRNA-Seq data analysis commonly includes **two-dimensional embeddings** where individual cells are visualised, and coloured according to unsupervised **clustering** assignments or computationally inferred **cell type** annotations. Such outcomes are sensitive to the **methods** (tool + version + parameter). The large **number of analysis steps** further complicates a comprehensive **reporting** of the methodology. It hinders the **traceability** of results and limits the **reusability** of published results and the possibility to **build upon** them.

The **reproducibility** of the analysis is a solution to overcome these issues. It can be achieved with **existing tools**. Here, we propose a **methodology** to conduct such an analysis, and provide a concrete application with the **analysis of a public dataset**.

## METHODS, CODE AND DATA AVAILABILITY

We re-analysed a public dataset to answer project-specific questions, while ensuring the reproducibility of the computational analysis, from the count matrices stage up to the ready-to-publish figures.

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Original publication behind the data: Wu et al., 2022, Science Advances

Count matrices: DOI: 10.5281/zenodo.15103192

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Code: DOI: 10.5281/zenodo.15591926 (R Markdown)

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aquarius: DOI: 10.5281/zenodo.15763900 (R package)

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Environment: DOI: 10.5281/zenodo.15512051 (Singularity/Apptainer)

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Analysis: DOI: 10.5281/zenodo.15281925 (R package)

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This work is associated with a document in press for ACM REP '25: Proceedings of the 3rd ACM Conference on Reproducibility and Replicability, under DOI: 10.1145/3736731.3746138