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ProbeSelect: selecting differentially expressed probes in transcriptional profile data
Raghavendra Hosur1, Suzanne Szak, Alice Thai
1Patient Stratification group and Genetics and Genomics group, Biogen Idec, Cambridge, MA, USA.
Bioinformatics (Oxford, England)
|December 17, 2013
Summary
This study introduces a statistical method to identify differentially expressed genes in heterogeneous patient populations. This approach enhances biomarker discovery by overcoming subtle human transcriptional differences and population variability.
Area of Science:
- Biostatistics
- Genomics
- Biomarker Discovery
Background:
- Transcriptional profiling is key for biomarker identification in patient samples.
- Population heterogeneity complicates the identification of true biomarkers due to subtle transcriptional differences and inherent variability.
Purpose of the Study:
- To propose a statistical technique for identifying differentially expressed probes in heterogeneous populations.
- To improve the statistical evidence for selecting relevant biomarkers.
Main Methods:
- Development of a simple statistical technique.
- Implementation of the algorithm in Java.
Main Results:
- The technique effectively identifies differentially expressed probes in heterogeneous samples.
- Improved ability to distinguish true biomarkers from population variability.
Conclusions:
- The proposed statistical method addresses limitations in biomarker discovery caused by population heterogeneity.
- This technique offers a more robust approach to identifying relevant biomarkers from transcriptional profiling data.
