edgeR：一個數字基因表達資料差異表達分析Bioconductor程式包

edgeR：一個數字基因表達資料差異表達分析Bioconductor程式包

人們希望在不久的將來，對於許多功能基因組學應用，新興的數字基因表達（digital gene expression，DGE）技術將超過微陣列技術。基本資料分析任務之一，特別是對於基因表達研究，涉及到確定是否有證據表明一個轉錄本或外顯子的計數在跨實驗條件下是顯著差異的。edgeR是一個研究重複計數資料差異表達的Bioconductor軟體包。一個過度離散的泊松模型被用於說明生物學可變性和技術可變性。經驗貝葉斯方法被用於減輕跨轉錄本的過度離散程度，改進了推斷的可靠性。該方法甚至能夠用最小重複水平使用，只要至少一個表型或實驗條件是重複的。該軟體可能具有測序資料之外的其他應用，例如蛋白質組多肽計數資料。可用性：程式包在遵循LGPL許可證下可以從Bioconductor網站（http://bioconductor.org/packages/release/bioc/html/edgeR.html）免費獲得。

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 影響因子: 4.531 發表日期: 20100101 官網 免費下載 全文下載