Deconfounding microarray analysis - Independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias
Deconfounding microarray analysis - Independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias
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DOI：
10.1055/s-0038-1634118
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发表时间：
2006-01-01
影响因子：
1.7
通讯作者：
Ziegler, A.
中科院分区：
文献类型：
--
作者：
Jacobsen, M.;Repsilber, D.;Ziegler, A.
目的：微阵列分析需要标准化的标本和评价程序，以达到可接受的结果。这种方法的一个主要局限性是由组织标本的细胞组成的异质性引起的，这经常混淆数据分析。我们引入了一个线性模型，以deconfounded基因表达数据从组织异质性的基因只表达由一个单一的细胞type.Methods：基因表达数据是deconfounded从组织异质性的影响，通过分析他们使用适当的线性回归模型。在我们的说明数据集中，使用流式细胞术测量组织异质性。通过真实的时间定量聚合酶链反应（qPCR）和微阵列分析平行测定基因表达数据。使用相应标志物基因的蛋白质定量来验证去干扰。对于我们的说明数据集，对来自结核病患者和对照的外周血单核细胞（PBMC）的细胞类型比例的定量揭示了两个研究组之间的B细胞和单核细胞比例的差异，从而揭示了所研究的组织的异质性。基因表达分析反映了这些差异的细胞型分布。填充适当的线性模型使我们能够从组织异质性效应中去发现测量的转录组水平。在单核细胞的情况下，可以提出在单细胞水平上的另外的差异表达。蛋白质定量验证了这些deconfounded results.Conclusions：Deconfounding的转录组分析细胞异质性大大提高了可解释性，因此转录组分析结果的有效性。
Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type.Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data ore determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes.Results. for our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Filling an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results.Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.