ArticleBioinformatics (Oxford, England)2014
The most informative spacing test effectively discovers biologically relevant outliers or multiple modes in expression.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Prognostic Significance of Major Histocompatibility Complex Class II Expression in Pediatric Adrenocortical Tumors: A St. Jude and Children's Oncology Group Study.Clinical cancer research : an official journal of the American Association for Cancer Research · 2016Trial
- Genome-wide cross-cancer analysis illustrates the critical role of bimodal miRNA in patient survival and drug responses to PI3K inhibitors.PLoS computational biology · 2022Article
- Finding associations in a heterogeneous setting: statistical test for aberration enrichment.Genome medicine · 2021Article
- How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data.Frontiers in molecular biosciences · 2021Review
- Discovery of regulatory noncoding variants in individual cancer genomes by using cis-X.Nature genetics · 2020Article
- Transcriptome profiling of patient derived xenograft models established from pediatric acute myeloid leukemia patients confirm maintenance of FLT3-ITD mutation.Leukemia & lymphoma · 2017Article
- Pan-cancer transcriptome analysis reveals long noncoding RNAs with conserved function.RNA biology · 2015Article
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7 authors.
Funding
Abstract
summarySeveral 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 CONTACT: stanley.pounds@stjude.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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