ArticleBioinformatics (Oxford, England)2020
Identification of population-level differentially expressed genes in one-phenotype data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.BMC bioinformatics · 2024Article
- A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings.Translational cancer research · 2024Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- An Improved Stacked Autoencoder for Metabolomic Data Classification.Computational intelligence and neuroscience · 2021Article
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11 authors.
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Abstract
motivationFor some specific tissues, such as the heart and brain, normal controls are difficult to obtain. Thus, studies with only a particular type of disease samples (one phenotype) cannot be analyzed using common methods, such as significance analysis of microarrays, edgeR and limma. The RankComp algorithm, which was mainly developed to identify individual-level differentially expressed genes (DEGs), can be applied to identify population-level DEGs for the one-phenotype data but cannot identify the dysregulation directions of DEGs.
resultsHere, we optimized the RankComp algorithm, termed PhenoComp. Compared with RankComp, PhenoComp provided the dysregulation directions of DEGs and had more robust detection power in both simulated and real one-phenotype data. Moreover, using the DEGs detected by common methods as the 'gold standard', the results showed that the DEGs detected by PhenoComp using only one-phenotype data were comparable to those identified by common methods using case-control samples, independent of the measurement platform. PhenoComp also exhibited good performance for weakly differential expression signal data. AVAILABILITY AND IMPLEMENTATION: The PhenoComp algorithm is available on the web at https://github.com/XJJ-student/PhenoComp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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