title: Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies.
abstract: BACKGROUND
  Leveraging 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.
  
  
  RESULTS
  We 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.
authors.affiliation: Department of Computer Science, Princeton University, Princeton, NJ 08544, USA and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
authors.forename: Young-suk
authors.initials: YS
authors.lastname: Lee
authors.forename: Arjun
authors.initials: A
authors.lastname: Krishnan
authors.forename: Qian
authors.initials: Q
authors.lastname: Zhu
authors.forename: Olga G
authors.initials: OG
authors.lastname: Troyanskaya
mesh_terms: D001499:Bayes Theorem; D002477:Cells / Q000145:classification*; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D009928:Organ Specificity
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