CARMA: A platform for analyzing microarray datasets that incorporate replicate measures.
CARMA: A platform for analyzing microarray datasets that incorporate replicate measures.
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DOI：
10.1186/1471-2105-7-149
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发表时间：
2006-03-17
影响因子：
3
通讯作者：
Hoying JB
中科院分区：
文献类型：
--
作者：
Greer KA;McReynolds MR;Brooks HL;Hoying JB
纳入统计模型，占实验变异性提供了一个必要的框架，解释微阵列数据。一个强大的实验设计加上方差分析（ANOVA）纳入一个模型，占已知来源的实验变异性，可以显着提高基因表达差异的测定和估计其意义。为了实现对微阵列数据进行方差分析的全部好处，我们开发了CARMA，一种微阵列分析平台，它可以读取大多数微阵列图像处理软件包生成的数据文件，使用用户定义的线性模型进行ANOVA，并产生易于解释的图形和数字结果。不需要对数据进行预处理，用户指定的参数控制分析的大部分方面，包括统计学显著性标准。该软件还执行位置和强度相关的lowess归一化，自动离群值检测和删除，并容纳丢失的数据。CARMA为每个测量的基因提供了清晰的定量和统计特征，可用于评估勉强可接受的测量，并提高微阵列结果解释的置信度。总的来说，将CARMA应用于包含重复测量的微阵列数据集有效地减少了被错误地鉴定为差异表达的基因的数量，并导致更稳健和可靠的分析。
The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust experimental design coupled with an analysis of variance (ANOVA) incorporating a model that accounts for known sources of experimental variability can significantly improve the determination of differences in gene expression and estimations of their significance. To realize the full benefits of performing analysis of variance on microarray data we have developed CARMA, a microarray analysis platform that reads data files generated by most microarray image processing software packages, performs ANOVA using a user-defined linear model, and produces easily interpretable graphical and numeric results. No pre-processing of the data is required and user-specified parameters control most aspects of the analysis including statistical significance criterion. The software also performs location and intensity dependent lowess normalization, automatic outlier detection and removal, and accommodates missing data. CARMA provides a clear quantitative and statistical characterization of each measured gene that can be used to assess marginally acceptable measures and improve confidence in the interpretation of microarray results. Overall, applying CARMA to microarray datasets incorporating repeated measures effectively reduces the number of gene incorrectly identified as differentially expressed and results in a more robust and reliable analysis.
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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
影响因子：
3.1
作者：
Ding, YY;Wilkins, D
通讯作者：
Wilkins, D
影响因子：
5.8
作者：
Rocke, DM;Durbin, B
通讯作者：
Durbin, B
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：
5.8
作者：
Baldi, P;Long, AD
通讯作者：
Long, AD