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 Videos

Detect tissue heterogeneity in gene expression data with BioQC.

Jitao David Zhang1, Klas Hatje2, Gregor Sturm2

  • 1Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, 4070, Switzerland. jitao_david.zhang@roche.com.

BMC Genomics
|April 6, 2017
PubMed
Summary

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

Cilia to basement membrane signaling is a biomechanical driver in models of autosomal dominant polycystic kidney disease.

The Journal of clinical investigation·2026
Same author

Tobemstomig, a Novel Bispecific Antibody, Preferentially Blocks PD-1 and LAG-3 on CD8 TILs to Expand Stem-like T Cells for Sustained Tumor Control.

Cancer research communications·2026
Same author

IBDome: An Integrated Molecular, Histopathological, and Clinical Atlas of Inflammatory Bowel Diseases.

Gastroenterology·2026
Same author

Tunable gene control via RNA splicing with a clinically approved small molecule.

Nature communications·2026
Same author

DUSP1 is a Key Driver of Disease Persistence and Potential Therapeutic Target in Hairy Cell Leukemia.

Blood advances·2026
Same author

HNF1B integrates signals in a feed-forward loop driving kidney disease progression.

Science (New York, N.Y.)·2026

Tissue heterogeneity in gene expression data can compromise results. BioQC is a new R/Bioconductor tool that efficiently detects and addresses this issue, improving data quality and reproducibility.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression data quality is crucial for accurate biological interpretation.
  • Tissue heterogeneity, contamination by non-target cells, is a common issue in gene expression profiling.
  • Existing methods often fail to detect tissue heterogeneity, impacting data reliability.

Purpose of the Study:

  • To introduce BioQC, a novel R/Bioconductor package for detecting tissue heterogeneity in gene expression data.
  • To provide a computationally efficient and sensitive tool for identifying sample contamination.
  • To enhance the quality and reproducibility of gene expression profiling studies.

Main Methods:

  • Development of BioQC, a software package implementing a Wilcoxon-Mann-Whitney test.
Keywords:
Gene expressionGene-set enrichment analysisQuality controlWilcoxon-Mann-Whitney test

Related Experiment Videos

  • Inclusion of over 150 pre-computed gene expression signatures for tissue-enriched genes.
  • Validation through simulation experiments and case studies using whole-organ and GTEx project data.

    • Main Results:

    • BioQC demonstrates high speed and sensitivity in detecting tissue heterogeneity.
    • Case studies confirmed BioQC's ability to identify contamination events, validated by quantitative RT-PCR.
    • Analysis of GTEx data revealed sample clustering and potential tissue heterogeneity issues.

    Conclusions:

    • Tissue heterogeneity is prevalent and often overlooked in gene expression data.
    • BioQC effectively integrates prior knowledge with a scalable algorithm to address this challenge.
    • BioQC is proposed as a primary tool for ensuring the quality and reproducibility of gene expression data.