ProbeSelect: selecting differentially expressed probes in transcriptional profile data.
Where this comes from
- Record sourced from PubMed, PMID 24336808.
- Also identified by DOI 10.1093/bioinformatics/btt720 and PMC identifier 3928527.
- Licence recorded as CC BY-NC.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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. The algorithm has been implemented in Java and available at www.sourceforge.net/projects/probeselect.
Medical subject headings
- Algorithms
- Gene Expression Profiling
- Multiple Sclerosis, Chronic Progressive
- Oligonucleotide Array Sequence Analysis
- Software