ArticleBMC genomics2017
Determining the optimal number of independent components for reproducible transcriptomic data analysis.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.Communications medicine · 2026Article
- Upfront whole blood transcriptional patterns in patients receiving immune checkpoint inhibitors associate with clinical outcome.Cancer immunology, immunotherapy : CII · 2025Article
- Leveraging principal component analysis to uncover urban pedestrian dynamics.Journal of geographical systems · 2025Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
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- PPIGCF: A Protein-Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection.Genes · 2023Article
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- Neural signatures of data-driven psychopathology dimensions at the transition to adolescence.European psychiatry : the journal of the Association of European Psychiatrists · 2022Article
- Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network.Frontiers in neuroscience · 2022Article
- Optimal dimensionality selection for independent component analysis of transcriptomic data.BMC bioinformatics · 2021Article
- Comparison of metabolic states using genome-scale metabolic models.PLoS computational biology · 2021Article
- Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.Nucleic acids research · 2021Article
- Independent component analysis recovers consistent regulatory signals from disparate datasets.PLoS computational biology · 2021Article
- Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.Frontiers in genetics · 2021Article
- Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application toJournal of the Royal Society, Interface · 2020Article
- Principle ERP reduction and analysis: Estimating and using principle ERP waveforms underlying ERPs across tasks, subjects and electrodes.NeuroImage · 2020Article
- Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations.Genome biology · 2020Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIndependent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data.
resultsHere we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets.
conclusionsWe suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.
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