Managing batch effects in high-throughput sequencing studies remains a persistent challenge, particularly when analyzing complex diseases such as endometriosis. A collaboration between Heinrich Heine University Düsseldorf (HHU), the University of California, San Francisco (UCSF), and Stanford
University has led to a new computational method designed to improve experimental design and data quality in such studies. The work was recently published in Cell Reports Methods.
Endometriosis affects millions worldwide, with tissue resembling the uterine lining growing outside the uterus. To better understand its cellular and molecular drivers, researchers at the ENACT Center, led by Linda Giudice and Marina Sirota at UCSF and Brice Gaudilliere and David Stevenson at Stanford, are applying single-nucleus RNA sequencing and related approaches to samples from hundreds of women. These analyses require batch processing, which, if not balanced, can introduce artifacts that obscure true biological signals.
The anticlustering framework, first developed at HHU in 2020 by Martin Papenberg and Gunnar Klau, was originally designed to evenly distribute samples across groups. The team has now extended the method with what Papenberg calls the “Must-Link Method,” ensuring that related samples, such as multiple tissues from the same patient, remain in the same batch. “Addressing the technical needs of the ENACT team requires, in addition to the previous scope of anticlust, that related samples, such as multiple tissue samples from the same patient, are grouped in the same batch to enable meaningful comparisons to be drawn for individual patients,” said Papenberg.
Klau added that they extended their method so that related samples can be kept in the same batch, while still distributing samples evenly across batches. “This prevents methodological bias and medical colleagues can draw conclusions from the data, which relate specifically to the influences of the genes on endometriosis,” he noted.
According to UCSF’s Tomiko Oskotsky, the tool offers a way to strengthen confidence in sequencing data. “By using anticlust to minimise batch effects through better experimental design, we are confident that the findings in our molecular data genuinely reflect the underlying biology.”
Publication Details
Papenberg M, et al. Anticlustering for Sample Allocation to Minimize Batch Effects. Cell Reports Methods (2025). DOI: 10.1016/ j.crmeth.2025.101137
University has led to a new computational method designed to improve experimental design and data quality in such studies. The work was recently published in Cell Reports Methods.
Endometriosis affects millions worldwide, with tissue resembling the uterine lining growing outside the uterus. To better understand its cellular and molecular drivers, researchers at the ENACT Center, led by Linda Giudice and Marina Sirota at UCSF and Brice Gaudilliere and David Stevenson at Stanford, are applying single-nucleus RNA sequencing and related approaches to samples from hundreds of women. These analyses require batch processing, which, if not balanced, can introduce artifacts that obscure true biological signals.
The anticlustering framework, first developed at HHU in 2020 by Martin Papenberg and Gunnar Klau, was originally designed to evenly distribute samples across groups. The team has now extended the method with what Papenberg calls the “Must-Link Method,” ensuring that related samples, such as multiple tissues from the same patient, remain in the same batch. “Addressing the technical needs of the ENACT team requires, in addition to the previous scope of anticlust, that related samples, such as multiple tissue samples from the same patient, are grouped in the same batch to enable meaningful comparisons to be drawn for individual patients,” said Papenberg.
Klau added that they extended their method so that related samples can be kept in the same batch, while still distributing samples evenly across batches. “This prevents methodological bias and medical colleagues can draw conclusions from the data, which relate specifically to the influences of the genes on endometriosis,” he noted.
According to UCSF’s Tomiko Oskotsky, the tool offers a way to strengthen confidence in sequencing data. “By using anticlust to minimise batch effects through better experimental design, we are confident that the findings in our molecular data genuinely reflect the underlying biology.”
Publication Details
Papenberg M, et al. Anticlustering for Sample Allocation to Minimize Batch Effects. Cell Reports Methods (2025). DOI: 10.1016/ j.crmeth.2025.101137