ArticleBMC bioinformatics2013
Robust PCA based method for discovering differentially expressed genes.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- GSTRPCA: irregular tensor singular value decomposition for single-cell multi-omics data clustering.Briefings in bioinformatics · 2024Article
- Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer.Scientific reports · 2021Article
- Joint Lp-Norm and LFrontiers in genetics · 2021Article
- Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis.Frontiers in genetics · 2019Article
- An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data.Genes · 2018Article
- Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering.Scientific reports · 2018Article
- An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.Scientific reports · 2017Article
- Consistent metagenomic biomarker detection via robust PCA.Biology direct · 2017Article
- Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation.BioMed research international · 2017Article
- Disentangling multidimensional spatio-temporal data into their common and aberrant responses.PloS one · 2015Article
- A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.PloS one · 2015Article
- A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.PloS one · 2014Article
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Authors and funding
6 authors.
Funding
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Abstract
How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.
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