Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 16, 2026

High-Quality Brain and Bone Marrow Nuclei Preparation for Single Nuclei Multiome Assays
07:59

High-Quality Brain and Bone Marrow Nuclei Preparation for Single Nuclei Multiome Assays

Published on: December 22, 2023

OSAT: a tool for sample-to-batch allocations in genomics experiments.

Li Yan1, Changxing Ma, Dan Wang

  • 1Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA. Li.Yan@RoswellPark.org

BMC Genomics
|December 12, 2012
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinically Sufficient Vitamin D Levels With Survival and Cardiovascular Outcomes in a Prospective Cohort of 3,995 Individuals Diagnosed With Invasive Breast Cancer.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

The Association Between Performance on the Emergency Medical Services In-Training Exam and Passing the Emergency Medical Services Certifying Exam.

AEM education and training·2025
Same author

Self-Reported COVID-19 Vaccine Status and Barriers for Pediatric Emergency Patients and Caregivers.

The western journal of emergency medicine·2025
Same author

VO2MAX, 6-minute walk, and muscle strength each correlate with frailty in US veterans.

Frontiers in physiology·2024
Same author

Common odds ratio test and interval estimation for stratified bilateral and unilateral data.

Statistical methods in medical research·2024
Same author

Testing the homogeneity of odds ratio across strata for combined bilateral and unilateral data.

PloS one·2024
Same journal

Different genomic footprint of small insertion-deletion and structural variants determines the genetic divergence of indica and japonica rice.

BMC genomics·2026
Same journal

From nurse bee to queen egg: RNA-seq analysis of Apis mellifera eggs shows dietary protein-dependent gene regulation.

BMC genomics·2026
Same journal

A genome-wide association study to identify the genetic loci underlying carbapenem resistance in Acinetobacter baumannii.

BMC genomics·2026
Same journal

Comparative transcriptome analysis to reveal key drought stress-responsive genes in sorghum (Sorghum bicolor (L.) Moench).

BMC genomics·2026
Same journal

Tissue identity is the dominant determinant of cross-species transferability of a porcine developmental programme.

BMC genomics·2026
Same journal

Characterization of mitochondrial genomes from three medicinal species of rutaceae and comparative analysis within the family: insights into evolution.

BMC genomics·2026
See all related articles

Batch effects are common in genomic studies. The Optimal Sample Assignment Tool (OSAT) is a new package that helps assign samples to batches to minimize these effects, even with incomplete data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Batch effects are a common source of unwanted variability in large-scale genomic experiments.
  • Proper experimental design is crucial to minimize batch effects by distributing biological groups and confounding factors evenly across batches.
  • Practical constraints often lead to unbalanced or incomplete sample collections, potentially exacerbating batch effects if sample-to-batch allocation is not carefully managed.

Purpose of the Study:

  • To develop an effective and user-friendly tool for optimizing sample-to-batch allocation in genomics studies.
  • To minimize the impact of batch effects by ensuring appropriate sample distribution across experimental batches.
  • To provide a solution for handling complex scenarios with unbalanced and incomplete sample data.

Main Methods:

More Related Videos

Cost-Efficient Transcriptomic-Based Drug Screening
06:40

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Related Experiment Videos

Last Updated: May 16, 2026

High-Quality Brain and Bone Marrow Nuclei Preparation for Single Nuclei Multiome Assays
07:59

High-Quality Brain and Bone Marrow Nuclei Preparation for Single Nuclei Multiome Assays

Published on: December 22, 2023

Cost-Efficient Transcriptomic-Based Drug Screening
06:40

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

  • Development of the Optimal Sample Assignment Tool (OSAT) as a Bioconductor package.
  • Implementation of algorithms for automated sample-to-batch allocation.
  • Optimization strategies focused on even distribution of biological variables and homogeneous distribution of confounding factors.

Main Results:

  • OSAT provides automated sample-to-batch allocation for genomics experiments.
  • The tool is designed to optimize the distribution of samples across batches.
  • It effectively handles both balanced and unbalanced/incomplete sample collection scenarios.

Conclusions:

  • OSAT facilitates sample allocation in genomics studies to mitigate batch effects.
  • By optimizing sample distribution, OSAT reduces confounding between batches and biological variables.
  • The tool is capable of managing challenging datasets with incomplete or unbalanced sample collections.