ArticleBMC genomics2015
Discovering monotonic stemness marker genes from time-series stem cell microarray data.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Identification of monotonically classifying pairs of genes for ordinal disease outcomes.Bioinformatics advances · 2026Article
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- Generating transcriptional regulatory networks from time-ordered stem cell differentiation RNA sequencing data.STAR protocols · 2022Article
- Unravelling the Complex Interplay of Transcription Factors Orchestrating Seed Oil Content inInternational journal of molecular sciences · 2021Article
- Computational Identification of Master Regulators Influencing Trypanotolerance in Cattle.International journal of molecular sciences · 2021Article
- Identification of the Complex Interplay Between Nematode-Related lncRNAs and Their Target Genes inFrontiers in plant science · 2021Article
- Decoding the differentiation of mesenchymal stem cells into mesangial cells at the transcriptomic level.BMC genomics · 2020Article
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- Identification of interactive molecules between antler stem cells and dermal papilla cells using an in vitro co-culture system.Journal of molecular histology · 2020Article
- Identification of Monotonically Differentially Expressed Genes across Pathologic Stages for Cancers.Journal of oncology · 2020Article
- Identification of monotonically differentially expressed genes for non-small cell lung cancer.BMC bioinformatics · 2019Article
- Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles.Scientific reports · 2019Article
- Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures.BioMed research international · 2019Review
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10 authors.
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Abstract
backgroundIdentification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.
resultsWe have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.
conclusionsWe have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/.
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