Abstract
The naïve use of expression ratios in high-throughput biological studies can greatly limit analytical outcome especially when sample size is small. In the worst-case scenario, with only one reference and one test state each (often due to the severe lack of study material); such limitations make it difficult to perform statistically meaningful analysis. Workarounds include the single sample Z-test or through network inference. Here, we describe a complementary plot-based approach for analysing such extremely small sized ratio (ESSR) data - a generalisation of the Bland-Altman plot, which we shall refer to as the Dodeca-Panels. Included in this paper is an R implementation of the Dodeca-Panels method.
Original language | English |
---|---|
Pages (from-to) | 268-280 |
Number of pages | 13 |
Journal | International Journal of Bioinformatics Research and Applications |
Volume | 11 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2015 Inderscience Enterprises Ltd.
ASJC Scopus Subject Areas
- Biomedical Engineering
- Health Informatics
- Clinical Biochemistry
- Health Information Management
Keywords
- Bioinformatics
- Clinical translation
- Drug and biomarker discovery
- Protein responders
- Proteomics
- Small ratios dataset