Title |
The most informative spacing test effectively discovers biologically relevant outliers or multiple modes in expression
|
---|---|
Published in |
Bioinformatics, January 2014
|
DOI | 10.1093/bioinformatics/btu039 |
Pubmed ID | |
Authors |
Iwona Pawlikowska, Gang Wu, Michael Edmonson, Zhifa Liu, Tanja Gruber, Jinghui Zhang, Stan Pounds |
Abstract |
Several outlier and subgroup identification statistics (OASIS) have been proposed to discover transcriptomic features with outliers or multiple modes in expression that are indicative of distinct biological processes or subgroups. Here, we borrow ideas from the OASIS methods in the bioinformatics and statistics literature to develop the 'most informative spacing test' (MIST) for unsupervised detection of such transcriptomic features. In an example application involving 14 cases of pediatric acute megakaryoblastic leukemia, MIST more robustly identified features that perfectly discriminate subjects according to gender or the presence of a prognostically relevant fusion-gene than did seven other OASIS methods in the analysis of RNA-seq exon expression, RNA-seq exon junction expression and micorarray exon expression data. MIST was also effective at identifying features related to gender or molecular subtype in an example application involving 157 adult cases of acute myeloid leukemia. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 33% |
Norway | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 8% |
Spain | 1 | 4% |
Unknown | 23 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 31% |
Student > Ph. D. Student | 7 | 27% |
Student > Master | 5 | 19% |
Student > Doctoral Student | 2 | 8% |
Other | 2 | 8% |
Other | 1 | 4% |
Unknown | 1 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 8 | 31% |
Biochemistry, Genetics and Molecular Biology | 6 | 23% |
Computer Science | 3 | 12% |
Nursing and Health Professions | 2 | 8% |
Immunology and Microbiology | 1 | 4% |
Other | 4 | 15% |
Unknown | 2 | 8% |