scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914145572487168 |
|---|---|
| author | Xu, Ping Wang, Zaitian Wang, Zhirui Li, Pengjiang Zhang, Ran Li, Gaoyang Xie, Hanyu Wang, Jiajia Zhou, Yuanchun Wang, Pengfei |
| author_facet | Xu, Ping Wang, Zaitian Wang, Zhirui Li, Pengjiang Zhang, Ran Li, Gaoyang Xie, Hanyu Wang, Jiajia Zhou, Yuanchun Wang, Pengfei |
| contents | Single-cell RNA sequencing (scRNA-seq) technology enables systematic delineation of cellular states and interactions, providing crucial insights into cellular heterogeneity. Building on this potential, numerous computational methods have been developed for tasks such as cell clustering, cell type annotation, and marker gene identification. To fully assess and compare these methods, standardized, analysis-ready datasets are essential. However, such datasets remain scarce, and variations in data formats, preprocessing workflows, and annotation strategies hinder reproducibility and complicate systematic evaluation of existing methods. To address these challenges, we present scUnified, an AI-ready standardized resource for single-cell RNA sequencing data that consolidates 13 high-quality datasets spanning two species (human and mouse) and nine tissue types. All datasets undergo standardized quality control and preprocessing and are stored in a uniform format to enable direct application in diverse computational analyses without additional data cleaning. We further demonstrate the utility of scUnified through experimental analyses of representative biological tasks, providing a reproducible foundation for the standardized evaluation of computational methods on a unified dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis Xu, Ping Wang, Zaitian Wang, Zhirui Li, Pengjiang Zhang, Ran Li, Gaoyang Xie, Hanyu Wang, Jiajia Zhou, Yuanchun Wang, Pengfei Genomics Artificial Intelligence Single-cell RNA sequencing (scRNA-seq) technology enables systematic delineation of cellular states and interactions, providing crucial insights into cellular heterogeneity. Building on this potential, numerous computational methods have been developed for tasks such as cell clustering, cell type annotation, and marker gene identification. To fully assess and compare these methods, standardized, analysis-ready datasets are essential. However, such datasets remain scarce, and variations in data formats, preprocessing workflows, and annotation strategies hinder reproducibility and complicate systematic evaluation of existing methods. To address these challenges, we present scUnified, an AI-ready standardized resource for single-cell RNA sequencing data that consolidates 13 high-quality datasets spanning two species (human and mouse) and nine tissue types. All datasets undergo standardized quality control and preprocessing and are stored in a uniform format to enable direct application in diverse computational analyses without additional data cleaning. We further demonstrate the utility of scUnified through experimental analyses of representative biological tasks, providing a reproducible foundation for the standardized evaluation of computational methods on a unified dataset. |
| title | scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis |
| topic | Genomics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25884 |