Integrating single-cell RNA-seq datasets with substantial batch effects
Karin Hrovatin; Amir Ali Moinfar; Luke Zappia; Shrey Parikh; Alejandro Tejada-Lapuerta; Benjamin J. Lengerich; Manolis Kellis; Fabian J. Theis · 2025 · BMC Genomics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Integration of single-cell RNA-sequencing (scRNA-seq) datasets is standard in scRNA-seq analysis. Nevertheless, current computational methods struggle to harmonize datasets across systems such as species, organoids and primary tissue, or different scRNA-seq protocols, including single-cell and single-nuclei. Conditional variational autoencoders (cVAE) are a popular integration method, however, existing strategies for stronger batch correction have limitations. Increasing the Kullback-Leibler divergence regularization does not improve integration and adversarial learning removes biological sign
Abstract by Karin Hrovatin; Amir Ali Moinfar; Luke Zappia; Shrey Parikh; Alejandro Tejada-Lapuerta; Benjamin J. Lengerich; Manolis Kellis; Fabian J. Theis, BMC Genomics (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s12864-025-12126-3