ArticleBMC genomics2015
MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression 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 7 papers.
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Who cites it
7 citing papers in PubMed.
- Robust and rigorous identification of tissue-specific genes by statistically extending tau score.BioData mining · 2022Article
- Data-driven detection of subtype-specific differentially expressed genes.Scientific reports · 2021Article
- Transcriptomic Time-Series Analyses of Gene Expression Profile During Zygotic Embryo Development inFrontiers in genetics · 2021Article
- A deconvolution method and its application in analyzing the cellular fractions in acute myeloid leukemia samples.BMC genomics · 2020Article
- The embryonic transcriptome of Arabidopsis thaliana.Plant reproduction · 2019Article
- Article
- Therapeutic Complement Targeting in ANCA-Associated Vasculitides and Thrombotic Microangiopathy.Biomedicine hubReview
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5 authors.
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Abstract
backgroundIdentification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and cell research. Marker genes are of great importance for determining cell identity, and for understanding tissue specific gene function and the molecular mechanisms underlying complex diseases.
resultsWe have developed a new bioinformatics tool called MGFM (Marker Gene Finder in Microarray data) to predict marker genes from microarray gene expression data. Marker genes are identified through the grouping of samples of the same type with similar marker gene expression levels. We verified our approach using two microarray data sets from the NCBI's Gene Expression Omnibus public repository encompassing samples for similar sets of five human tissues (brain, heart, kidney, liver, and lung). Comparison with another tool for tissue-specific gene identification and validation with literature-derived established tissue markers established functionality, accuracy and simplicity of our tool. Furthermore, top ranked marker genes were experimentally validated by reverse transcriptase-polymerase chain reaction (RT-PCR). The sets of predicted marker genes associated with the five selected tissues comprised well-known genes of particular importance in these tissues. The tool is freely available from the Bioconductor web site, and it is also provided as an online application integrated into the CellFinder platform ( http://cellfinder.org/analysis/marker ).
conclusionsMGFM is a useful tool to predict tissue/cell type marker genes using microarray gene expression data. The implementation of the tool as an R-package as well as an application within CellFinder facilitates its use.
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