Pan-cancer analysis of systematic batch effects on somatic sequence variations
Open accessAbstract
BACKGROUND: The Cancer Genome Atlas (TCGA) is a comprehensive database that includes multi-layered cancer genome profiles. Large-scale collection of data inevitably generates batch effects introduced by differences in processing at various stages from sample collection to data generation. However, batch effects on the sequence variation and its characteristics have not been studied extensively. RESULTS: We systematically evaluated batch effects on somatic sequence variations in pan-cancer TCGA data, revealing 999 somatic variants that were batch-biased with statistical significance (P < 0.00001, Fisher's exact test, false discovery rate ≤ 0.0027). Most of the batch-biased variants were associated with specific sample plates. The batch-biased variants, which had a unique mutational spectrum with frequent indel-type mutations, preferentially occurred at sites prone to sequencing errors, e.g., in long homopolymer runs. Non-indel type batch-biased variants were frequent at splicing sites with the unique consensus motif sequence 'TTDTTTAGTT'. Furthermore, some batch-biased variants occur in known cancer genes, potentially causing misinterpretation of mutation profiles. CONCLUSIONS: Our strategy for identifying batch-biased variants and characterising sequence patterns might be useful in eliminating false variants and facilitating correct interpretation of sequence profiles.
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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From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [25]
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RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach
Poore, G. D., Kopylova, E., Zhu, Q., Carpenter, C., Fraraccio, S., Wandro, S., Kosciolek, T., Janssen, S., Metcalf, J., Song, S. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2095-1 · Cited by 989 · see citing works
RetractedThe impact of site-specific digital histology signatures on deep learning model accuracy and bias
Howard, F. M., Dolezal, J., Kochanny, S., Schulte, J., Chen, H., Heij, L., Huo, D., Nanda, R., Olopade, O. I., Kather, J. N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24698-1 · Cited by 330 · see citing works
- Authors
- Choi, J.-H., Hong, S.-E., & Woo, H. G.
- Affiliations
- Ajou University (KR)
- Corresponding author
- Woo, Hyun Goo
- Venue
- BMC Bioinformatics
- Published
- 2017-04-11
- Volume
- 18
- Issue
- 1
- Pages
- 211-211
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC5387285
- OpenAlex
- W2606002900
- Cited by
- 14
- References
- 32
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 2 · 2019: 1
- FWCI
- 0.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- AJOU Open Repository (Ajou University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Ministry of Health and Welfare (H15C1551)
- National Research Foundation of Korea (NRF-2012R1A5A2048183)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- batch effects, somatic mutations, pan-cancer analysis, TCGA, sequencing errors, indel mutations, splicing sites, mutation spectrum, cancer genes, variant calling
- MeSH terms
- Humans, Mutation, Neoplasms, Reproducibility of Results, Sequence Analysis, DNA, Genomics, Databases, Genetic
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