ArticleJournal of mathematical biology2019
Orthogonal joint sparse NMF for microarray data analysis.
Article in Journal of mathematical biology, 2019. 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.
- Identification of differentially expressed genes in RNA-seq data via semi-rigid orthogonal sparse KL-NMTF.BMC bioinformatics · 2026Article
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Meta-analysis highlights the key drought responsive genes in genes: PEPC and TaSAG7 are hubs response networks.Journal, genetic engineering & biotechnology · 2022Article
- An NMF-Based Methodology for Selecting Biomarkers in the Landscape of Genes of Heterogeneous Cancer-Associated Fibroblast Populations.Bioinformatics and biology insights · 2020Article
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3 authors.
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Abstract
The 3D microarrays, generally known as gene-sample-time microarrays, couple the information on different time points collected by 2D microarrays that measure gene expression levels among different samples. Their analysis is useful in several biomedical applications, like monitoring dose or drug treatment responses of patients over time in pharmacogenomics studies. Many statistical and data analysis tools have been used to extract useful information. In particular, nonnegative matrix factorization (NMF), with its natural nonnegativity constraints, has demonstrated its ability to extract from 2D microarrays relevant information on specific genes involved in the particular biological process. In this paper, we propose a new NMF model, namely Orthogonal Joint Sparse NMF, to extract relevant information from 3D microarrays containing the time evolution of a 2D microarray, by adding additional constraints to enforce important biological proprieties useful for further biological analysis. We develop multiplicative updates rules that decrease the objective function monotonically, and compare our approach to state-of-the-art NMF algorithms on both synthetic and real data sets.
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