scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis

2025-09-30

AI Summary

Current single-cell RNA sequencing (scRNA-seq) data lack standardized, ready-to-use resources; heterogeneous formats, inconsistent preprocessing pipelines, and divergent annotation strategies severely hinder reproducibility and fair benchmarking of computational methods. To address this, we introduce scUnified—a first-of-its-kind, cross-species (human/mouse), multi-tissue (nine tissues), AI-ready standardized scRNA-seq resource integrating 13 high-quality datasets. All data undergo uniform quality control, gene filtering, normalization, and batch correction, and are released in H5AD format—fully compatible with Scanpy, Seurat, and other mainstream analysis frameworks. Rigorous quality validation and empirical evaluation demonstrate that scUnified significantly improves stability and reproducibility in key tasks including cell clustering and marker gene identification. By providing a rigorously curated, harmonized benchmark dataset, scUnified establishes a reliable foundation for methodological benchmarking and fosters equitable, transparent evaluation of scRNA-seq analysis tools.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Semi-Supervised Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Standardized datasets are scarce for scRNA-seq method evaluation
• Data format variations hinder reproducibility and systematic comparisons
• Lack of uniform preprocessing complicates computational analysis workflows

Innovation

• Standardized quality control and preprocessing workflows
• Uniform data format for direct computational analysis
• Consolidated datasets across species and tissue types