ArticleBioMed research international2014
Microarray-based RNA profiling of breast cancer: batch effect removal improves cross-platform consistency.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis.Molecular medicine (Cambridge, Mass.) · 2022Pooled it
- Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration.Journal of cellular and molecular medicine · 2024Article
- Ensemble-based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow long-term risk of metastases.Cancer medicine · 2024Article
- Which decisions affect cohort distribution in COVID-19 data analytics?AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Multi-omics integration analysis unveils heterogeneity in breast cancer at the individual level.Cell cycle (Georgetown, Tex.) · 2023Article
- A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer.Cancer informatics · 2023Article
- Classification with 2-D convolutional neural networks for breast cancer diagnosis.Scientific reports · 2022Article
- Diagnostic signature, subtype classification, and immune infiltration of key m6A regulators in osteomyelitis patients.Frontiers in genetics · 2022Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Robustifying genomic classifiers to batch effects via ensemble learning.Bioinformatics (Oxford, England) · 2021Article
- An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus.Arthritis research & therapy · 2020Article
- Unlocking the transcriptomic potential of formalin-fixed paraffin embedded clinical tissues: comparison of gene expression profiling approaches.BMC bioinformatics · 2020Article
- Clinical and Survival Impact of Sex-Determining Region Y-Box 2 in Colorectal Cancer: An Integrated Analysis of the Immunohistochemical Study and Bioinformatics Analysis.Journal of oncology · 2020Article
- The pathogenesis shared between abdominal aortic aneurysms and intracranial aneurysms: a microarray analysis.Neurosurgical review · 2018Article
- Progress in the clinical detection of heterogeneity in breast cancer.Cancer medicine · 2016Review
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5 authors.
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Abstract
Microarray is a powerful technique used extensively for gene expression analysis. Different technologies are available, but lack of standardization makes it challenging to compare and integrate data. Furthermore, batch-related biases within datasets are common but often not tackled. We have analyzed the same 234 breast cancers on two different microarray platforms. One dataset contained known batch-effects associated with the fabrication procedure used. The aim was to assess the significance of correcting for systematic batch-effects when integrating data from different platforms. We here demonstrate the importance of detecting batch-effects and how tools, such as ComBat, can be used to successfully overcome such systematic variations in order to unmask essential biological signals. Batch adjustment was found to be particularly valuable in the detection of more delicate differences in gene expression. Furthermore, our results show that prober adjustment is essential for integration of gene expression data obtained from multiple sources. We show that high-variance genes are highly reproducibly expressed across platforms making them particularly well suited as biomarkers and for building gene signatures, exemplified by prediction of estrogen-receptor status and molecular subtypes. In conclusion, the study emphasizes the importance of utilizing proper batch adjustment methods when integrating data across different batches and platforms.
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