ArticleBMC bioinformatics2018
Data-driven human transcriptomic modules determined by independent component analysis.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales.Annual review of biophysics · 2025Review
- Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales.ArXiv · 2024Article
- Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.BMC bioinformatics · 2024Article
- CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model.Briefings in bioinformatics · 2023Article
- Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.PLoS computational biology · 2022Article
- BloodGen3Module: blood transcriptional module repertoire analysis and visualization using R.Bioinformatics (Oxford, England) · 2021Article
- Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.Nature communications · 2021Article
- Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.Frontiers in genetics · 2021Article
- Correcting for experiment-specific variability in expression compendia can remove underlying signals.GigaScience · 2020Article
- Application of Transcriptional Gene Modules to Analysis ofG3 (Bethesda, Md.) · 2020Article
- Responsible, practical genomic data sharing that accelerates research.Nature reviews. Genetics · 2020Review
- A modular framework for the development of targeted Covid-19 blood transcript profiling panels.Journal of translational medicine · 2020Article
- Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations.Genome biology · 2020Article
- Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets.International journal of molecular sciences · 2019Review
- Genetics, epigenetics and redox homeostasis in rhabdomyosarcoma: Emerging targets and therapeutics.Redox biology · 2019Review
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2 authors.
Funding
Abstract
backgroundAnalyzing the human transcriptome is crucial in advancing precision medicine, and the plethora of over half a million human microarray samples in the Gene Expression Omnibus (GEO) has enabled us to better characterize biological processes at the molecular level. However, transcriptomic analysis is challenging because the data is inherently noisy and high-dimensional. Gene set analysis is currently widely used to alleviate the issue of high dimensionality, but the user-defined choice of gene sets can introduce biasness in results. In this paper, we advocate the use of a fixed set of transcriptomic modules for such analysis. We apply independent component analysis to the large collection of microarray data in GEO in order to discover reproducible transcriptomic modules that can be used as features for machine learning. We evaluate the usability of these modules across six studies, and demonstrate (1) their usage as features for sample classification, and also their robustness in dealing with small training sets, (2) their regularization of data when clustering samples and (3) the biological relevancy of differentially expressed features.
resultsWe identified 139 reproducible transcriptomic modules, which we term fundamental components (FCs). In studies with less than 50 samples, FC-space classification model outperformed their gene-space counterparts, with higher sensitivity (p < 0.01). The models also had higher accuracy and negative predictive value (p < 0.01) for small data sets (less than 30 samples). Additionally, we observed a reduction in batch effects when data is clustered in the FC-space. Finally, we found that differentially expressed FCs mapped to GO terms that were also identified via traditional gene-based approaches.
conclusionsThe 139 FCs provide biologically-relevant summarization of transcriptomic data, and their performance in low sample settings suggest that they should be employed in such studies in order to harness the data efficiently.
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