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Independent component analysis of microarray data in the study of endometrial cancer

Oncogene volume 23, pages 6677–6683 (2004)Cite this article

Abstract

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.

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Acknowledgements

We thank Dr Andrew Sharkey and Amanda Evans for their assistance in design and construction of the arrays used in these studies, and Addenbrooke's hospital, Cambridge, for their collaboration. This work was carried out in the MRC Reproductive Angiogenesis Cooperative Group and was supported in part by program grant G9623012. C Holland acknowledges support from an MRC Research Training Fellowship (G84/5733) and the Raymond and Beverly Sackler Studentship Award 2002. D Kreil acknowledges support by an MRC Research Training Fellowship (G81/555).

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Authors and Affiliations

  1. Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, CB2 2SW, UK

    Samir A Saidi, D Stephen Charnock-Jones & Stephen K Smith

  2. Department of Pathology, University of Cambridge, Cambridge, CB1 1QP, UK

    Cathrine M Holland, D Stephen Charnock-Jones, Cristin G Print & Stephen K Smith

  3. Department of Physics, Inference Group, Cavendish Laboratory, Cambridge, CB3 0HE, UK

    David P Kreil & David J C MacKay

  4. Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK

    David P Kreil

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  1. Samir A Saidi
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Correspondence to Samir A Saidi.

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Saidi, S., Holland, C., Kreil, D. et al. Independent component analysis of microarray data in the study of endometrial cancer. Oncogene 23, 6677–6683 (2004). https://doi.org/10.1038/sj.onc.1207562

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