ArticleBMC bioinformatics2016
GFS: fuzzy preprocessing for effective gene expression analysis.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multivariate meta-analysis reveals global transcriptomic signatures underlying distinct human naive-like pluripotent states.PloS one · 2021Pooled it
- Minor intron splicing is critical for survival of lethal prostate cancer.Molecular cell · 2023Article
- The importance of batch sensitization in missing value imputation.Scientific reports · 2023Article
- Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier.STAR protocols · 2022Article
- Doppelgänger spotting in biomedical gene expression data.iScience · 2022Article
- Article
- Perspectives for better batch effect correction in mass-spectrometry-based proteomics.Computational and structural biotechnology journal · 2022Review
- Filtering of the Gene Signature as the Predictors of Cisplatin-Resistance in Ovarian Cancer.Iranian journal of biotechnology · 2021Article
- An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules.Frontiers in genetics · 2019Article
- Detecting hidden batch factors through data-adaptive adjustment for biological effects.Bioinformatics (Oxford, England) · 2018Article
- SPSNet: subpopulation-sensitive network-based analysis of heterogeneous gene expression data.BMC systems biology · 2018Article
- Can Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?Computational psychiatry (Cambridge, Mass.) · 2017Article
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Abstract
backgroundGene expression data produced on high-throughput platforms such as microarrays is susceptible to much variation that obscures useful biological information. Therefore, preprocessing data with a suitable normalization method is necessary, and has a direct and massive impact on the quality of downstream data analysis. However, it is known that standard normalization methods perform poorly, specially in the presence of substantial batch effects and heterogeneity in gene expression data.
resultsWe present Gene Fuzzy Score (GFS), a simple preprocessing technique, that is able to largely reduce obscuring variation while retaining useful biological information. Using four sets of publicly available datasets containing batch effects and heterogeneity, we compare GFS with three standard normalization techniques as well as raw gene expression. Each method is evaluated with respect to the quality, consistency, and biological coherence of its processed output. It is found that GFS outperforms other transformation techniques in all three aspects.
conclusionOur approach to preprocessing is a stronger alternative to popular normalization techniques. We demonstrate that it achieves the essential goal of preprocessing - it is effective at making expression values from multiple samples comparable, even when they are from separate platforms, in independent batches, or belong to a heterogeneous phenotype.
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