A single-cell RNA-seq catalog of ground truth gene coregulation
- 1Tahoe Therapeutics
Abstract
Single-cell RNA-sequencing measurements are uniquely well-poised to identify coregulated gene transcription. Coregulation should be apparent as correlation of expression levels, but at which unit to quantify expression for calculation of correlations is not clear. To enable evaluation of normalization methods for identification of coregulation we have curated and preprocessed 35 scRNA-seq samples with known regulature relationships into a catalog. These datasets contain promoter-reporter data representing known positive coregulation and B cells where isotypic mutual exclusion of {kappa} and {lambda} light chains represent known negative coregulation. As an example, we use the catalog to evaluate seven normalization methods. Different normalization methods perform better for positive or negative coregulation, and results indicate implementation choices for advanced normalization methods The catalog may serve as a resource for evaluating novel proposed normalization methods and is available at https://huggingface.co/datasets/valsv/scrna-coregulation-benchmark
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BibTeX
@misc{svensson2026singlecell,
title = {{A single-cell RNA-seq catalog of ground truth gene coregulation}},
author = {V. Svensson},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.752692},
url = {https://www.biorxiv.org/content/10.64898/2026.09.18.752692v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A single-cell RNA-seq catalog of ground truth gene coregulation
AU - V. Svensson
PY - 2026
DA - 2026/09/18
PB - bioRxiv
DO - 10.64898/2026.09.18.752692
UR - https://www.biorxiv.org/content/10.64898/2026.09.18.752692v1
AB - Single-cell RNA-sequencing measurements are uniquely well-poised to identify coregulated gene transcription. Coregulation should be apparent as correlation of expression levels, but at which unit to quantify expression for calculation of correlations is not clear. To enable evaluation of normalization methods for identification of coregulation we have curated and preprocessed 35 scRNA-seq samples with known regulature relationships into a catalog. These datasets contain promoter-reporter data representing known positive coregulation and B cells where isotypic mutual exclusion of {kappa} and {lambda} light chains represent known negative coregulation. As an example, we use the catalog to evaluate seven normalization methods. Different normalization methods perform better for positive or negative coregulation, and results indicate implementation choices for advanced normalization methods The catalog may serve as a resource for evaluating novel proposed normalization methods and is available at https://huggingface.co/datasets/valsv/scrna-coregulation-benchmark
ER -