ArticleBMC bioinformatics2009
Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
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- Comparative Mucous miRomics in Cynoglossus semilaevis Related to Vibrio harveyi Caused Infection.Marine biotechnology (New York, N.Y.) · 2021Article
- The Encystment-Related MicroRNAs and Its Regulation Molecular Mechanism inInternational journal of molecular sciences · 2020Article
- miRNA Profiling of Circulating Small Extracellular Vesicles From Subarachnoid Hemorrhage Rats Using Next-Generation Sequencing.Frontiers in cellular neuroscience · 2020Article
- Research of the mechanism on miRNA193 in exosomes promotes cisplatin resistance in esophageal cancer cells.PloS one · 2020Article
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Abstract
backgroundThe Audic-Claverie method 1 has been and still continues to be a popular approach for detection of differentially expressed genes in the SAGE framework. The method is based on the assumption that under the null hypothesis tag counts of the same gene in two libraries come from the same but unknown Poisson distribution. The problem is that each SAGE library represents only a single measurement. We ask: Given that the tag count samples from SAGE libraries are extremely limited, how useful actually is the Audic-Claverie methodology? We rigorously analyze the A-C statistic that forms a backbone of the methodology and represents our knowledge of the underlying tag generating process based on one observation.
resultsWe show that the A-C statistic and the underlying Poisson distribution of the tag counts share the same mode structure. Moreover, the K-L divergence from the true unknown Poisson distribution to the A-C statistic is minimized when the A-C statistic is conditioned on the mode of the Poisson distribution. Most importantly, the expectation of this K-L divergence never exceeds 1/2 bit.
conclusionA rigorous underpinning of the Audic-Claverie methodology has been missing. Our results constitute a rigorous argument supporting the use of Audic-Claverie method even though the SAGE libraries represent very sparse samples.
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