Journal
BIOINFORMATICS
Volume 29, Issue 23, Pages 3036-3044Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btt529
Keywords
-
Categories
Funding
- National Science Foundation (NSF) CAREER award [DBI-0546275]
- National Institutes of Health (NIH) [R01 GM071966, R01 HG005998, T32 HG003284]
- National Institute of General Medical Sciences (NIGMS) Center of Excellence [P50 GM071508]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available