URSA (Unveiling RNA Sample Annotation) simultaneously estimates the probabilities that a given sample is associated with a particular tissue or cell-type. Individual cell-type models were constructed from more than ten thousand manually curated samples from GEO (Gene Expression Omnibus) , and then aggregated using Bayesian Correction previously described in Barutcuoglu et al. 2006. This method has been shown effective for both array-based and sequence-based genome-scale experiments.
Reference: Lee YS, Krishnan A, Zhu Q, Troyanskaya OG. Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies. Bioinformatics. 2013 Sep 12.
URSAHD (Unveiling RNA Sample Annotation for Human Diseases) measures hundreds of disease-specific signatures in a single gene expression profile. Each disease-specific model (i.e. gene weights) were computed based on thousands of clinical samples from GEO.
Reference: Lee YS, Krishnan A, Theesfeld CL, Rust J, Chang C, Oughtred R, Kristensen VN, Dolinski K, Troyanskaya OG. Genome-wide characterization of the human disease landscape. manuscript in preperation.
URSA(HD) was created by the Laboratory for bioinformatics and Functional Genomics in the Lewis-Sigler Institue for Integrative Genomics at Princeton University.