Dataset Information
ProbeSelect: selecting differentially expressed probes in transcriptional profile data.
Ontology highlight
ABSTRACT: Summary
Transcriptional profiling still remains one of the most popular techniques for identifying relevant biomarkers in patient samples. However, heterogeneity in the population leads to poor statistical evidence for selection of most relevant biomarkers to pursue. In particular, human transcriptional differences can be subtle, making it difficult to tease out real differentially expressed biomarkers from the variability inherent in the population. To address this issue, we propose a simple statistical technique that identifies differentially expressed probes in heterogeneous populations as compared with controls.Availability and implementation
The algorithm has been implemented in Java and available at www.sourceforge.net/projects/probeselect.
SUBMITTER: Hosur R
PROVIDER: S-EPMC3928527 | biostudies-literature | 2014 Feb
REPOSITORIES: biostudies-literature
Publications
ProbeSelect: selecting differentially expressed probes in transcriptional profile data.
Bioinformatics (Oxford, England) 20131213 4
<h4>Summary</h4>Transcriptional profiling still remains one of the most popular techniques for identifying relevant biomarkers in patient samples. However, heterogeneity in the population leads to poor statistical evidence for selection of most relevant biomarkers to pursue. In particular, human transcriptional differences can be subtle, making it difficult to tease out real differentially expressed biomarkers from the variability inherent in the population. To address this issue, we propose a s ...[more]