ArticleBMC bioinformatics2010
Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Carbohydrate composition of infant formula and glycemic regulation in early infancy using continuous glucose monitoring: cross-sectional evidence of altered glucose patterns with corn syrup solid-based formulas.The American journal of clinical nutrition · 2026Observational
- Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.BMC bioinformatics · 2024Article
- A primer on correlation-based dimension reduction methods for multi-omics analysis.Journal of the Royal Society, Interface · 2023Review
- Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data.BMC bioinformatics · 2023Article
- Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease.Heliyon · 2023Article
- Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.Frontiers in genetics · 2022Article
- Selecting the right therapeutic target for kidney disease.Frontiers in pharmacology · 2022Review
- Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks.Molecules (Basel, Switzerland) · 2020Article
- Unsupervised dimensionality reduction for exposome research.Current opinion in environmental science & health · 2020Article
- Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations.Genome biology · 2020Article
- The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.Frontiers in microbiology · 2020Review
- Multi-cell ECM compaction is predictable via superposition of nonlinear cell dynamics linearized in augmented state space.PLoS computational biology · 2019Article
- Conserved transcriptomic profile between mouse and human colitis allows unsupervised patient stratification.Nature communications · 2019Article
- Overlapping clustering of gene expression data using penalized weighted normalized cut.Genetic epidemiology · 2018Article
- On the Use ofComputational and mathematical methods in medicine · 2018Article
- Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.BMC bioinformatics · 2017Article
- Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images.NeuroImage. Clinical · 2017Article
- Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data.Scientific reports · 2016Article
- A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data.Genomics data · 2016Article
- Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.The Journal of membrane biology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVisualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.
resultsA systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods.
conclusionsLocally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.