ArticleBioinformatics (Oxford, England)2013
Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Systematic tissue annotations of genomics samples by modeling unstructured metadata.Nature communications · 2022Article
- CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology.iScience · 2021Article
- A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes.Nucleic acids research · 2020Article
- Supervised learning is an accurate method for network-based gene classification.Bioinformatics (Oxford, England) · 2020Article
- A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.Cell systems · 2019Article
- Mining data and metadata from the gene expression omnibus.Biophysical reviews · 2019Review
- ALE: automated label extraction from GEO metadata.BMC bioinformatics · 2017Article
- Comparative transcriptomics in human and mouse.Nature reviews. Genetics · 2017Review
- Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications.Oncogene · 2017Article
- Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation.BioMed research international · 2017Article
- Tissue enrichment analysis for C. elegans genomics.BMC bioinformatics · 2016Article
- Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states.BMC genomics · 2016Article
- ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- RNA-Seq and microarray analysis of the Xenopus inner ear transcriptome discloses orthologous OMIM(®) genes for hereditary disorders of hearing and balance.BMC research notes · 2015Article
- Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets.Nucleic acids research · 2015Article
- Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.PLoS computational biology · 2015Article
- Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer.Nucleic acids research · 2015Article
- Sparse representation for tumor classification based on feature extraction using latent low-rank representation.BioMed research international · 2014Article
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4 authors.
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Abstract
motivationLeveraging gene expression data through large-scale integrative analyses for multicellular organisms is challenging because most samples are not fully annotated to their tissue/cell-type of origin. A computational method to classify samples using their entire gene expression profiles is needed. Such a method must be applicable across thousands of independent studies, hundreds of gene expression technologies and hundreds of diverse human tissues and cell-types.
resultsWe present Unveiling RNA Sample Annotation (URSA) that leverages the complex tissue/cell-type relationships and simultaneously estimates the probabilities associated with hundreds of tissues/cell-types for any given gene expression profile. URSA provides accurate and intuitive probability values for expression profiles across independent studies and outperforms other methods, irrespective of data preprocessing techniques. Moreover, without re-training, URSA can be used to classify samples from diverse microarray platforms and even from next-generation sequencing technology. Finally, we provide a molecular interpretation for the tissue and cell-type models as the biological basis for URSA's classifications.
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