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Visualisation and Modelling of High-Dimensional Cancerous Gene Expression Dataset

Author

Listed:
  • Abhishek Bhola

    (Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Sector 12, Chandigarh 160012, India)

  • Shailendra Singh

    (Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Sector 12, Chandigarh 160012, India)

Abstract

The increase in the number of dimensions of cancerous gene expression dataset causes an increase in complexity, misinterpretation and decrease in the visualisation of the particular dataset for further analysis. Therefore, dimensionality reduction, visualisation and modelling tasks of these dataset become challenging. In this paper, a framework is developed which helps to understand, visualise and model high-dimensional cancerous gene expression dataset into lower dimensions which may be helpful in revealing cancer mechanism and diagnosis. Initially, cancerous gene expression datasets are preprocessed to make them complete, precise and efficient; and principal component analysis is applied for dimensionality reduction and visualisation purpose. The regression is used to model the cancerous gene expression dataset so that type of association (linear or nonlinear) and directions between gene profiles may be estimated. To assess the performance of the developed framework, three different types of cancerous gene expression datasets are taken namely: breast (GEO Acc. No. GDS5076), lung (GEO Acc. No. GDS5040) and prostate (GEO Acc. No. GDS5072) which are publicly available. To validate the results of the regression the cross-validation method is used. The results revealed that a linear approach is to be used for prostate cancer dataset and nonlinear approach for breast and lung cancer datasets in finding an association between gene pairs.

Suggested Citation

  • Abhishek Bhola & Shailendra Singh, 2019. "Visualisation and Modelling of High-Dimensional Cancerous Gene Expression Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-22, March.
    • Handle: RePEc:wsi:jikmxx:v:18:y:2019:i:01:n:s0219649219500011
    DOI: 10.1142/S0219649219500011
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    References listed on IDEAS

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    1. Dai Jian J & Lieu Linh & Rocke David, 2006. "Dimension Reduction for Classification with Gene Expression Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-21, February.
    2. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    3. Witten, Daniela M. & Tibshirani, Robert, 2010. "A Framework for Feature Selection in Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 713-726.
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