ArticleBioinformatics (Oxford, England)2017
Imputing gene expression to maximize platform compatibility.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Bioinformatics analysis of PSAT1 loss identifies downstream pathways regulated in EGFR mutant NSCLC and a selective gene signature for predicting the risk of relapse.Oncology letters · 2025Article
- TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus.Journal of integrative bioinformatics · 2024Article
- A pairwise strategy for imputing predictive features when combining multiple datasets.Bioinformatics (Oxford, England) · 2023Article
- A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes.Nucleic acids research · 2020Article
- Article
- Data-driven human transcriptomic modules determined by independent component analysis.BMC bioinformatics · 2018Article
- A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.Bioinformatics (Oxford, England) · 2018Article
- Prognostic Characteristics of MACC1 Expression in Epithelial Ovarian Cancer.BioMed research international · 2018Article
- A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation.Frontiers in pharmacology · 2018Article
- Decentralized Learning Framework of Meta-Survival Analysis for Developing Robust Prognostic Signatures.JCO clinical cancer informatics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Microarray measurements of gene expression constitute a large fraction of publicly shared biological data, and are available in the Gene Expression Omnibus (GEO). Many studies use GEO data to shape hypotheses and improve statistical power. Within GEO, the Affymetrix HG-U133A and HG-U133 Plus 2.0 are the two most commonly used microarray platforms for human samples; the HG-U133 Plus 2.0 platform contains 54 220 probes and the HG-U133A array contains a proper subset (21 722 probes). When different platforms are involved, the subset of common genes is most easily compared. This approach results in the exclusion of substantial measured data and can limit downstream analysis. To predict the expression values for the genes unique to the HG-U133 Plus 2.0 platform, we constructed a series of gene expression inference models based on genes common to both platforms. Our model predicts gene expression values that are within the variability observed in controlled replicate studies and are highly correlated with measured data. Using six previously published studies, we also demonstrate the improved performance of the enlarged feature space generated by our model in downstream analysis. Availability and Implementation: The gene inference model described in this paper is available as a R package (affyImpute), which can be downloaded at http://simtk.org/home/affyimpute. Contact: rbaltman@stanford.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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