ArticleBMC genomics2017
Detect tissue heterogeneity in gene expression data with BioQC.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
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- Transcriptome remodelling and changes in growth and cardiometabolic phenotype result following Grb10a knockdown in the early life of the zebrafish.Cellular and molecular life sciences : CMLS · 2025Article
- The RNA mThe EMBO journal · 2025Article
- Genome-wide allele-specific expression in multi-tissue samples from healthy male baboons reveals the transcriptional complexity of mammals.Cell genomics · 2025Article
- Impact of Subcutaneous Versus Orthotopic Implantations on Patient-Derived Xenograft Transcriptomic Profiles.Cancer research communications · 2025Article
- A comprehensive analysis framework for evaluating commercial single-cell RNA sequencing technologies.Nucleic acids research · 2025Article
- A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.PLoS computational biology · 2025Article
- Loss of immune cell identity with age inferred from large atlases of single cell transcriptomes.Aging cell · 2024Article
- Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+T cells in the tumor microenvironment and predicts clinical outcome in early-phase and late-phase clinical trials.Journal for immunotherapy of cancer · 2024Article
- Transcriptional profiling upon T cell stimulation reveals down-regulation of inflammatory pathways in T and B cells in SLE versus Sjögren's syndrome.NPJ systems biology and applications · 2023Article
- Automatic quality control of single-cell and single-nucleus RNA-seq using valiDrops.NAR genomics and bioinformatics · 2023Article
- Programmable eukaryotic protein synthesis with RNA sensors by harnessing ADAR.Nature biotechnology · 2023Article
- The RNA mNature structural & molecular biology · 2023Article
- Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.Toxicological sciences : an official journal of the Society of Toxicology · 2022Article
- Oncogenic gene expression and epigenetic remodeling of cis-regulatory elements in ASXL1-mutant chronic myelomonocytic leukemia.Nature communications · 2022Article
- Pharmacodynamics and molecular correlates of response to glofitamab in relapsed/refractory non-Hodgkin lymphoma.Blood advances · 2022Article
- Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.NAR genomics and bioinformatics · 2021Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted.
resultsWe introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies. Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR. Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity.
conclusionsOur experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data.
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