CellMix: a comprehensive toolbox for gene expression deconvolution
CellMix: a comprehensive toolbox for gene expression deconvolution
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DOI：
10.1093/bioinformatics/btt351
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发表时间：
2013-09-01
期刊：
影响因子：
5.8
通讯作者：
Seoighe, Cathal
中科院分区：
文献类型：
--
作者：
Gaujoux, Renaud;Seoighe, Cathal
基因表达数据通常由异质生物样品生成，所述异质生物样品由多种细胞或组织类型以不同比例组成，每种细胞或组织类型都有助于全局基因表达。这种异质性是标准分析如差异表达分析中的主要混杂因素，其中组成细胞的相对比例的差异可能阻止或偏倚细胞特异性差异的检测。全局基因表达的计算解卷积是昂贵的物理样品分离技术的一种有吸引力的替代方案，并且能够在细胞类型水平上对潜在的生物过程进行更详细的分析。为了促进和推广这些方法的应用，我们开发了CellMix，这是一个R软件包，它将大多数最先进的去卷积方法整合到一个直观和可扩展的框架中，提供了一个单一的入口点来探索，评估和解开异质样品中的基因表达数据。
Gene expression data are typically generated from heterogeneous biological samples that are composed of multiple cell or tissue types, in varying proportions, each contributing to global gene expression. This heterogeneity is a major confounder in standard analysis such as differential expression analysis, where differences in the relative proportions of the constituent cells may prevent or bias the detection of cell-specific differences. Computational deconvolution of global gene expression is an appealing alternative to costly physical sample separation techniques and enables a more detailed analysis of the underlying biological processes at the cell-type level. To facilitate and popularize the application of such methods, we developed CellMix, an R package that incorporates most state-of-the-art deconvolution methods, into an intuitive and extendible framework, providing a single entry point to explore, assess and disentangle gene expression data from heterogeneous samples.