ArticleArchives of toxicology2015
Identification of sample annotation errors in gene expression datasets.
Article in Archives of toxicology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Identification of metabolism-associated molecular classification for effect and prognosis in lung adenocarcinoma based on multidatabases including the cancer genome atlas and gene expression omnibus.SAGE open medicine · 2025Article
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- Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer.Frontiers in immunology · 2023Article
- The effects of sequencing depth on the assembly of coding and noncoding transcripts in the human genome.BMC genomics · 2022Article
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- A novel HIF1α-STIL-FOXM1 axis regulates tumor metastasis.Journal of biomedical science · 2022Article
- Integrated Analysis Reveals the Potential Significance of HDAC Family Genes in Lung Adenocarcinoma.Frontiers in genetics · 2022Article
- Mining GEO and TCGA Database for Immune Microenvironment of Lung Squamous Cell Carcinoma Patients With or Without Chemotherapy.Frontiers in oncology · 2022Article
- Comprehensive analysis of the prognostic value and immune implications of the TTK gene in lung adenocarcinoma: a meta-analysis and bioinformatics analysis.Animal cells and systems · 2022Article
- A community effort to identify and correct mislabeled samples in proteogenomic studies.Patterns (New York, N.Y.) · 2021Article
- Large-scale labeling and assessment of sex bias in publicly available expression data.BMC bioinformatics · 2021Article
- Paired-like homeodomain 2B contributes to tumour progression and anti-autophagy in human lung cancer.American journal of cancer research · 2021Article
- CD73 Severed as a Potential Prognostic Marker and Promote Lung Cancer Cells MigrationFrontiers in genetics · 2021Article
- Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas.Disease markers · 2021Article
- Highlight report: Role of PD-L1 in never-smokers.EXCLI journal · 2019Article
- Highlight report: Toxicogenomics atlas of rat hepatotoxicants.EXCLI journal · 2018Article
- HYSYS: have you swapped your samples?Bioinformatics (Oxford, England) · 2017Article
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The comprehensive transcriptomic analysis of clinically annotated human tissue has found widespread use in oncology, cell biology, immunology, and toxicology. In cancer research, microarray-based gene expression profiling has successfully been applied to subclassify disease entities, predict therapy response, and identify cellular mechanisms. Public accessibility of raw data, together with corresponding information on clinicopathological parameters, offers the opportunity to reuse previously analyzed data and to gain statistical power by combining multiple datasets. However, results and conclusions obviously depend on the reliability of the available information. Here, we propose gene expression-based methods for identifying sample misannotations in public transcriptomic datasets. Sample mix-up can be detected by a classifier that differentiates between samples from male and female patients. Correlation analysis identifies multiple measurements of material from the same sample. The analysis of 45 datasets (including 4913 patients) revealed that erroneous sample annotation, affecting 40 % of the analyzed datasets, may be a more widespread phenomenon than previously thought. Removal of erroneously labelled samples may influence the results of the statistical evaluation in some datasets. Our methods may help to identify individual datasets that contain numerous discrepancies and could be routinely included into the statistical analysis of clinical gene expression data.
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