ArticleCancer informatics2013
Monitoring of technical variation in quantitative high-throughput datasets.
Article in Cancer informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Immune and molecular landscape behind non-response to Mycophenolate Mofetil and Azathioprine in lupus nephritis therapy.Research square · 2024Article
- Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset.Frontiers in immunology · 2024Article
- Analysis of a Four-Component Competing Endogenous RNA Network Reveals Potential Biomarkers in Gastric Cancer: An Integrated Systems Biology and Experimental Investigation.Advanced biomedical research · 2023Article
- Comprehensive competitive endogenous RNA network analysis revealsIranian journal of basic medical sciences · 2022Article
- Dynamics of Melanoma-Associated Epitope-Specific CD8+ T Cells in the Blood Correlate With Clinical Outcome Under PD-1 Blockade.Frontiers in immunology · 2022Article
- Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade.Science (New York, N.Y.) · 2021Article
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- Molecular analyses of triple-negative breast cancer in the young and elderly.Breast cancer research : BCR · 2021Article
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- Major Histocompatibility Complex Genes as Therapeutic Opportunity for Immune Cold Molecular Cancer Subtypes.Journal of immunology research · 2020Review
- Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors.Frontiers in immunology · 2020Review
- Transcriptomic Analysis Reveals Prognostic Molecular Signatures of Stage I Melanoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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