Independent component analysis of microarray data in the study of endometrial cancer
Independent component analysis of microarray data in the study of endometrial cancer
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DOI：
10.1038/sj.onc.1207562
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发表时间：
2004-08-26
期刊：
影响因子：
8
通讯作者：
Smith, SK
中科院分区：
文献类型：
--
作者：
Saidi, SA;Holland, CM;Smith, SK
基因微阵列技术在筛选差异基因表达方面非常有效，因此已成为癌症分子研究的热门工具。当应用于肿瘤时，分子特征可能与临床特征相关，例如对化疗的反应。然而，利用由微阵列产生的大量数据是困难的，并且构成了这种方法的进步的主要挑战。独立分量分析（伊卡）是一种现代统计方法，它使我们能够更好地理解这种复杂和噪声测量环境中的数据。该技术有可能显着提高所得数据的质量，并提高后续分析的生物学有效性。我们对31例绝经后子宫内膜活检标本进行了微阵列实验，包括11例良性和20例恶性样本。我们将伊卡与主成分分析（PCA）、Cyber-T和SAM等既定方法进行了比较。我们表明，伊卡产生的模式，清楚地描述了恶性样本的研究，在对比PCA。此外，伊卡提高了子宫内膜癌中差异表达基因的生物学有效性，与Cyber-T和SAM相比。特别是，涉及脂质代谢的几个基因，在子宫内膜癌中的差异表达，只有使用这种方法被发现。这份报告强调了伊卡在微阵列数据分析中的潜力。
Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.