title: Analysing extremely small sized ratio datasets.
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.
authors.affiliation: 1 Cardiovascular Division, Harvard Medical School, 25 Shattuck St Boston, MA 02115, USA.
authors.forename: Piero
authors.initials: P
authors.lastname: Ricchiuto
authors.affiliation: 2 Singapore Institute for Clinical Sciences, Brenner Centre for Molecular Medicine, 30 Medical Drive, 117609 Singapore; Department of Pharmacology, National University of Singapore, MD11 CRC 05-34, 117597 Singapore.
authors.forename: Judy C G
authors.initials: JC
authors.lastname: Sng
authors.affiliation: 3 Cardiovascular Division, Harvard Medical School, 25 Shattuck, St Boston, MA 02115, USA; School of Computing, National University of Singapore, COM1 13 Computing Drive, 117417 Singapore.
authors.forename: Wilson Wen Bin
authors.initials: WW
authors.lastname: Goh
mesh_terms: D035843:Biomedical Research / Q000379:methods* / Q000592:standards*; D019295:Computational Biology / Q000379:methods* / Q000592:standards*; D057166:High-Throughput Screening Assays; D018401:Sample Size
keywords: bioinformatics; biomarker discovery; clinical translation; drug discovery; protein responders; proteomics; small ratio datasets