Monitoring of Technical Variation in Quantitative High-Throughput Datasets
Open accessAbstract
High-dimensional datasets can be confounded by variation from technical sources, such as batches. Undetected batch effects can have severe consequences for the validity of a study's conclusion(s). We evaluate high-throughput RNAseq and miRNAseq as well as DNA methylation and gene expression microarray datasets, mainly from the Cancer Genome Atlas (TCGA) project, in respect to technical and biological annotations. We observe technical bias in these datasets and discuss corrective interventions. We then suggest a general procedure to control study design, detect technical bias using linear regression of principal components, correct for batch effects, and re-evaluate principal components. This procedure is implemented in the R package swamp, and as graphical user interface software. In conclusion, high-throughput platforms that generate continuous measurements are sensitive to various forms of technical bias. For such data, monitoring of technical variation is an important analysis step.
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Metagenomic TCGA data show substantial batch effects similar to those in expression and human genomic data.
“Substantial batch effects are known in TCGA expression and human genomic data25,26 and were replicated in metagenomic data (Fig. 1c, Extended Data Figs. 1b,d).”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [26]
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References 27 · Cited by 74
- Authors
- Lauss, M., Visne, I., Kriegner, A., Ringnér, M., Jönsson, G., & Höglund, M.
- Affiliations
- Lund University (SE)
- AIT Austrian Institute of Technology GmbH (AT)
- Department of Oncology, Clinical Sciences, Lund University, Sweden.
- Corresponding author
- Lauss, Martin
- Venue
- Cancer Informatics
- Publisher
- SAGE Publications
- Published
- 2013-01
- Volume
- 12
- Pages
- CIN.S12862
- ISSN
- 1176-9351 (ISSN-L)
- PMC
- PMC3785384
- OpenAlex
- W2062094438
- Cited by
- 74
- References
- 27
- Citations by year
- 2025: 5 · 2024: 8 · 2023: 7 · 2022: 3 · 2021: 11 · 2020: 6 · 2019: 2 · 2018: 7 · 2017: 6 · 2016: 8 · 2015: 5 · 2014: 5 · 2013: 1
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Cancer Informatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Lund University Publications (Lund University) (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- technical variability, batch effects, technical bias, high-throughput data, RNA-seq, miRNA sequencing, DNA methylation, gene expression microarrays, principal component regression, TCGA, research design, bias correction
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