Recommendations for improving single cell GEO record annotations
Rui Fu, Kent Riemondy, Sidhant Puntambekar
RNA Bioscience Initative, University of Colorado School of Medicine2022-02-21
recs.RmdRecommendations for depositing scRNA-seq datasets
For investigators and reviewers
Require that analysts provide a metadata table containing cell-level metadata and a count matrix with RNA abundance measurements. The cell-level metadata should contain the cell identifiers present in the matrix and provide the inferred cell-type or other cell-level annotations described in the associated publication. A binary object saved from the analysis framework could also be supplied (.rds for R or .h5ad for Python).
When reviewing single-cell sequencing studies, ensure that the authors have deposited the proper cell-level metadata alongside the raw data into a suitable repository (e.g. GEO, ArrayExpress).
Encouraging previous depositors of single-cell sequencing data to update their records with cell-level metadata, if it was not included in the original submission.
For journals
Include language about requirements/recommendation for external single-cell datasets to contain proper cell-level metadata.
Ask reviewers to review material deposited to external data repositories.
For data repositories
Public repositories should introduce a standardized annotation that specifies that the dataset contains single cell data. For GEO, commonly used single cell sequencing methods could be added to the library strategy annotation (e.g. scRNA-seq, snRNA-seq, CITE-seq, etc.).
Updating submission guidelines to require metadata with cell-level annotations for single cell dataset submissions. For GEO, this would be accomplished by updating the “Processed data files” requirements to outline required data types for single-cell sequencing submissions.
“For single-cell sequencing data, in addition to standard count matrices (genes-by-cells), we expect users to deposit metadata with cell-level annotations generated during the course of analysis.”