Bioinformatics 2014 May 22;30(10):1400-8. Epub 2014 Jan 22.
Departments of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA, Institue of Mathematics, University of Silesia, Katowice, Poland, Department of Computational Biology and Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA.
Summary: 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.
Availability: MIST will be freely available in the OASIS R package at http://www.stjuderesearch.org/site/depts/biostats
Supplementary Information: Supplementary data are available at Bioinformatics online.