Stability of gene contributions and identification of outliers in multivariate analysis of microarray data.
Stability of gene contributions and identification of outliers in multivariate analysis of microarray data.
复制标题
基因贡献的稳定性和对微阵列数据的多元分析中异常值的识别。
DOI：
10.1186/1471-2105-9-289
复制
发表时间：
2008-06-20
影响因子：
3
通讯作者：
Brutsche, Martin H.
中科院分区：
文献类型：
--
作者：
Baty, Florent;Jaeger, Daniel;Preiswerk, Frank;Schumacher, Martin M.;Brutsche, Martin H.
多元排序方法是探索微阵列数据中复杂数据结构的有力工具。这些方法与常见的逐基因方法相比具有几个优点。然而，由于其探索性的性质，多变量排序方法不允许直接的基因的稳定性的统计检验。在这项研究中，我们开发了一种计算效率高的算法：i）基因贡献的显着性评估和ii）在微阵列数据的多变量分析中识别样本离群值。该方法是基于使用的restaurant方法，包括自举和jackknifing。开发了R函数的统计软件包。该软件包包括用于推断基因贡献的统计显著性和识别样本中离群值的工具。该方法被成功地应用到三个已发表的数据集与不同水平的信号强度。将其相关性与其他方法进行了比较。总体而言，它被证明是特别有效的微阵列数据的稳定性的评价。
Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes. In this study, we developed a computationally efficient algorithm for: i) the assessment of the significance of gene contributions and ii) the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples. The methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data.
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DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
3.7
作者：
GREENACRE, M;HASTIE, T
通讯作者：
HASTIE, T
影响因子：
5.3
作者：
Westad, F;Hersleth, M;Martens, H
通讯作者：
Martens, H
影响因子：
5.8
作者：
Culhane, AC;Perrière, G;Higgins, DG
通讯作者：
Higgins, DG