Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach.
Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach.
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DOI：
10.1186/1471-2105-11-27
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发表时间：
2010-01-14
影响因子：
3
通讯作者：
Jacobsen M
中科院分区：
文献类型：
--
作者：
Repsilber D;Kern S;Telaar A;Walzl G;Black GF;Selbig J;Parida SK;Kaufmann SH;Jacobsen M
对于异质组织，例如血液，基因表达的测量受到所涉及细胞类型的相对比例的影响。结论必须依赖于对同质细胞群的基因表达信号的估计，例如通过应用显微解剖、荧光激活细胞分选或计算机解混。我们使用来自相同供体样本的血液和分选细胞的实验基因表达数据研究了非负矩阵分解算法的可行性和有效性。我们的目标是优化有关差异表达基因检测的算法，并使其能够用于在反向调控基因的困难情况下进行分类。这对于鉴定异质组织中的候选生物标志物非常重要。涉及从这些数据估计的噪声参数的实验数据和模拟研究表明，为了有效检测差异基因表达，分位数归一化和使用非对数数据是最佳的。我们证明了从单个样本的基因表达数据预测构成细胞类型的比例的可行性，这是基于去混杂的分类方法的先决条件。报告使用和不使用去混杂结果的分类交叉验证错误以及样本大小依赖性。提供算法、模拟和分析脚本的实现。针对难以检测的生物标志物以及怀疑因不同比例的细胞类型造成混杂的情况，提出了在非对数数据上使用分位数归一化的不去相关的去混杂算法。在这种情况下，去混杂排序方法可以用作其他统计学习方法的有力替代或补充，以在现实的噪声条件下和适度的样本量下定义生物医学分子诊断和预测的候选生物标志物。
For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.
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DOI：
10.1073/pnas.2536479100
发表时间：
2004-01-13
影响因子：
11.1
作者：
Stuart, RO;Wachsman, W;Mercola, D
通讯作者：
Mercola, D
影响因子：
5.8
作者：
Ghosh, D
通讯作者：
Ghosh, D
影响因子：
20.3
作者：
Watkins NA;Gusnanto A;de Bono B;De S;Miranda-Saavedra D;Hardie DL;Angenent WG;Attwood AP;Ellis PD;Erber W;Foad NS;Garner SF;Isacke CM;Jolley J;Koch K;Macaulay IC;Morley SL;Rendon A;Rice KM;Taylor N;Thijssen-Timmer DC;Tijssen MR;van der Schoot CE;Wernisch L;Winzer T;Dudbridge F;Buckley CD;Langford CF;Teichmann S;Göttgens B;Ouwehand WH;Bloodomics Consortium
通讯作者：
Bloodomics Consortium
影响因子：
5.8
作者：
Ritchie, Matthew E.;Silver, Jeremy;Smyth, Gordon K.
通讯作者：
Smyth, Gordon K.
影响因子：
1.7
作者：
Jacobsen, M.;Repsilber, D.;Ziegler, A.
通讯作者：
Ziegler, A.