ArticleBioinformatics (Oxford, England)2017
Molecular signatures that can be transferred across different omics platforms.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- A Bayesian Informative Shrinkage Approach for Large-scale Multiple Hypothesis Testing (BISHOT): with Applications in Differential Analysis of Omics Data.bioRxiv : the preprint server for biology · 2025Article
- Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine.NPJ digital medicine · 2022Article
- Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning.Reviews in cardiovascular medicine · 2022Article
- Breast tumor stiffness instructs bone metastasis via maintenance of mechanical conditioning.Cell reports · 2021Article
- Integrative computational epigenomics to build data-driven gene regulation hypotheses.GigaScience · 2020Article
- Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- Platform independent protein-based cell-of-origin subtyping of diffuse large B-cell lymphoma in formalin-fixed paraffin-embedded tissue.Scientific reports · 2020Article
- Constraining classifiers in molecular analysis: invariance and robustness.Journal of the Royal Society, Interface · 2020Article
- Expression Concordance of 325 Novel RNA Biomarkers between Data Generated by NanoString nCounter and Affymetrix GeneChip.Disease markers · 2019Article
- From hype to reality: data science enabling personalized medicine.BMC medicine · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMolecular signatures for treatment recommendations are well researched. Still it is challenging to apply them to data generated by different protocols or technical platforms.
resultsWe analyzed paired data for the same tumors (Burkitt lymphoma, diffuse large B-cell lymphoma) and features that had been generated by different experimental protocols and analytical platforms including the nanoString nCounter and Affymetrix Gene Chip transcriptomics as well as the SWATH and SRM proteomics platforms. A statistical model that assumes independent sample and feature effects accounted for 69-94% of technical variability. We analyzed how variability is propagated through linear signatures possibly affecting predictions and treatment recommendations. Linear signatures with feature weights adding to zero were substantially more robust than unbalanced signatures. They yielded consistent predictions across data from different platforms, both for transcriptomics and proteomics data. Similarly stable were their predictions across data from fresh frozen and matching formalin-fixed paraffin-embedded human tumor tissue. AVAILABILITY AND IMPLEMENTATION: The R-package 'zeroSum' can be downloaded at https://github.com/rehbergT/zeroSum . Complete data and R codes necessary to reproduce all our results can be received from the authors upon request. CONTACT: rainer.spang@ur.de.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.