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Abstract：
CONCLUSIONS: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data.
BACKGROUND: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
BACKGROUND: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
摘要：
结论：预计新兴的数字基因表达（DGE）技术将在不久的将来在许多功能基因组学应用中超越微阵列技术。基础数据分析任务之一，尤其是基因表达研究,涉及确定是否有证据表明转录物或外显子的计数在不同的实验条件下存在显着差异。edgeR是Bioconductor软件包，用于检查复制计数数据的差异表达。过度分散的泊松模型用于解释生物学和技术变异性。经验贝叶斯方法用于缓和转录本之间的过度分散程度，提高推理的可靠性。该方法甚至可以在最低程度的复制中使用，条件是复制至少一种表型或实验条件。该软件可能具有测序数据以外的其他应用程序，如蛋白质组肽计数数据。
背景：该软件包可在Bioconductor网站（http://biocorductor.org）的LGPL许可下免费获得。
背景：该软件包可在Bioconductor网站（http://biocorductor.org）的LGPL许可下免费获得。
