edgeR：一个数字基因表达数据差异表达分析Bioconductor程序包

人们希望在不久的将来，对于许多功能基因组学应用，新兴的数字基因表达（digital gene expression，DGE）技术将超过微阵列技术。基本数据分析任务之一，特别是对于基因表达研究，涉及到确定是否有证据表明一个转录本或外显子的计数在跨实验条件下是显著差异的。edgeR是一个研究重复计数数据差异表达的Bioconductor软件包。一个过度离散的泊松模型被用于说明生物学可变性和技术可变性。经验贝叶斯方法被用于减轻跨转录本的过度离散程度，改进了推断的可靠性。该方法甚至能够用最小重复水平使用，只要至少一个表型或实验条件是重复的。该软件可能具有测序数据之外的其他应用，例如蛋白质组多肽计数数据。可用性：程序包在遵循LGPL许可证下可以从Bioconductor网站（http://bioconductor.org/packages/release/bioc/html/edgeR.html）免费获得。

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Robinson Mark D   McCarthy Davis J   Smyth Gordon K  

SUMMARY: 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. AVAILABILITY: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

pmid: 19910308 Bioinformatics 影响因子: 4.531 发表日期: 20100101 官网 免费下载 全文下载