limma powers differential expression analyses for RNA-sequencing and microarray studies.
limma powers differential expression analyses for RNA-sequencing and microarray studies.
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DOI：
10.1093/nar/gkv007
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发表时间：
2015-04-20
影响因子：
14.9
通讯作者：
Smyth GK
中科院分区：
文献类型：
--
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
limma是一个R/Bioconductor软件包，为分析基因表达实验的数据提供了一个集成的解决方案。它包含丰富的功能，用于处理复杂的实验设计和信息借用，以克服小样本量的问题。在过去的十年中，limma一直是通过微阵列和高通量PCR数据的差异表达分析进行基因发现的热门选择。该软件包包含特别强大的阅读、规范化和探索此类数据的工具。最近，limma的能力在两个重要的方向上得到了显著的扩展。首先，该软件包现在可以对RNA测序（RNA-seq）数据进行差异表达和差异剪接分析。以前仅限于微阵列数据的所有下游分析工具现在也可用于RNA-seq。这些功能允许用户使用非常相似的管道分析RNA-seq和微阵列数据。其次，该软件包现在能够以各种方式超越传统的基因表达分析，分析共调控基因组或高阶表达特征方面的表达谱。这为基因表达差异的生物学解释提供了增强的可能性。本文回顾了limma包的原理和设计，总结了新的和历史的特性，重点是最近的增强和以前没有描述过的特性。
limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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影响因子：
14.9
作者：
Rehrauer H;Zoller S;Schlapbach R
通讯作者：
Schlapbach R
影响因子：
2.2
作者：
Langsrud, O
通讯作者：
Langsrud, O
影响因子：
4.4
作者：
Choi J
通讯作者：
Choi J
DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J