ArticleScientific reports2016
Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales.Annual review of biophysics · 2025Review
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- Statistically principled feature selection for single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
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- Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.Toxicology and applied pharmacology · 2024Article
- General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning.Human brain mapping · 2024Article
- Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.Biomedicines · 2023Article
- General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning.bioRxiv : the preprint server for biology · 2023Article
- Insights into Repeated Renal Injury Using RNA-Seq with Two New RPTEC Cell Lines.International journal of molecular sciences · 2023Article
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- NAPS Fusion: A framework to overcome experimental data limitations to predict human performance and cognitive task outcomes.An international journal on information fusion · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset. In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations. Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal.
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