Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies

Cited 17 time in webofscience Cited 0 time in scopus
  • Hit : 53
  • Download : 0
Motivation: 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.
Publisher
OXFORD UNIV PRESS
Issue Date
2013-12
Language
English
Article Type
Article
Citation

BIOINFORMATICS, v.29, no.23, pp.3036 - 3044

ISSN
1367-4803
DOI
10.1093/bioinformatics/btt529
URI
http://hdl.handle.net/10203/280421
Appears in Collection
BiS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 17 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0