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Reusable Single Cell for Iterative Epigenomic Analyses

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Persistent hindrances to data re-use in single-cell genomics.

Sanja Rogic^1,2, Xinrui Xiang Yu^1,2, Brianna Xu^1,2

¹Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.

Scientific Data

|June 24, 2026

Summary

Reusing single-cell RNA sequencing (scRNA-seq) data is challenging due to poor data availability and lack of cell-type labels. Current sharing practices hinder effective data reuse and necessitate improved repository standards.

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Area of Science:

Genomics
Bioinformatics
Data Science

Background:

Single-cell RNA sequencing (scRNA-seq) generates high-resolution gene expression data.
Public repositories like the Gene Expression Omnibus (GEO) host numerous scRNA-seq studies.
Effective data reuse is crucial for advancing biological research and validating findings.

Purpose of the Study:

To evaluate the reusability of publicly available scRNA-seq datasets.
To assess the availability and quality of processed data and cell-type annotations in scRNA-seq studies.
To identify limitations in current data sharing practices for scRNA-seq data.

Main Methods:

Semi-automated and manual curation of scRNA-seq studies from GEO.
Focus on 10x Genomics-based datasets.
Assessment of cell-level expression count matrices and cell-type annotations.

Main Results:

Only approximately 40% of scRNA-seq studies provided usable processed count data.
Fewer than 10% of studies included author-provided cell-type labels.
Most raw data required complex heuristics for re-analysis, limiting automated reuse.

Conclusions:

Current scRNA-seq data sharing practices are insufficient for effective reuse.
Repositories need to enforce stricter submission requirements for processed data and cell-type annotations.
Improved data standards are essential for maximizing the value of public scRNA-seq datasets.