Jaumot Joaquim, Tauler Romà, Gargallo Raimundo
Department of Analytical Chemistry, Universitat de Barcelona, Diagonal 647, E-08028 Barcelona, Spain.
Anal Biochem. 2006 Nov 1;358(1):76-89. doi: 10.1016/j.ab.2006.07.028. Epub 2006 Aug 17.
In this work, the application of a multivariate curve resolution procedure based on alternating least squares optimization (MCR-ALS) for the analysis of data from DNA microarrays is proposed. For this purpose, simulated and publicly available experimental data sets have been analyzed. Application of MCR-ALS, a method that operates without the use of any training set, has enabled the resolution of the relevant information about different cancer lines classification using a set of few components; each of these defined by a sample and a pure gene expression profile. From resolved sample profiles, a classification of samples according to their origin is proposed. From the resolved pure gene expression profiles, a set of over- or underexpressed genes that could be related to the development of cancer diseases has been selected. Advantages of the MCR-ALS procedure in relation to other previously proposed procedures such as principal component analysis are discussed.
在这项工作中，提出了一种基于交替最小二乘优化的多元曲线分辨方法（MCR-ALS）用于分析DNA微阵列数据。为此，对模拟的和公开可用的实验数据集进行了分析。MCR-ALS是一种无需使用任何训练集即可运行的方法，它能够使用少量成分集解析有关不同癌症细胞系分类的相关信息；其中每个成分由一个样本和一个纯基因表达谱定义。根据解析出的样本谱，提出了根据样本来源进行分类的方法。从解析出的纯基因表达谱中，选择了一组可能与癌症疾病发展相关的过表达或低表达基因。讨论了MCR-ALS方法相对于其他先前提出的方法（如主成分分析）的优势。
