Independent component analysis of Alzheimer's DNA microarray gene expression data

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dc.contributor.authorLiu, Qingzhong
dc.contributor.authorChen, Zhongxue
dc.contributor.authorVanderburg, Charles R
dc.contributor.authorRogers, Jack T.
dc.contributor.authorHuang, Xudong
dc.contributor.authorMou, Xiaoyang
dc.contributor.authorWei, Kong
dc.date.accessioned2022-01-25T17:04:16Z
dc.date.available2022-01-25T17:04:16Z
dc.date.issued2009-01-28
dc.descriptionThe article was originally published in Molecular Neurodegeneration. doi:10.1186/1750-1326-4-5
dc.description.abstractBackground: Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, highdimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics. Results: ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support vector machine recursive feature elimination (SVM-RFE) methods, which are widely used in microarray data analysis, ICA can identify more AD-related genes. Furthermore, we have validated and identified many genes that are associated with AD pathogenesis. Conclusion: We demonstrated that ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that lead to the construction of potential AD-related pathogenic pathways. Our computing results also validated that the ICA model outperformed PCA and the SVM-RFE method. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.
dc.description.sponsorshipWK would like to express her gratitude for the supports from the Research Foundation of Shanghai Municipal Education Commission (No. 06FZ012 and No.2008098), the National Natural Science Foundation of China (No. 60801060), and Radiology Department of Brigham and Women's Hospital (BWH). XH is supported by grants from the NIA/NIH (5R21AG028850), Alzheimer's Association (IIRG-07-60397), and the research funds from BWH Radiology Department.
dc.description.subjectmultiple cellular pathways
dc.description.subjectDNA microarray gene expression data
dc.description.subjectAlzheimer disease
dc.description.subjecthigher-order statistics
dc.description.subjectmultifactorial complex diseases
dc.description.subjectpolygenic complex diseases
dc.identifier.citationKong W, Mou X, Liu Q, Chen Z, Vanderburg C, Rogers J, and Huang X (2009). Independent component analysis of Alzheimer’s DNA microarray gene expression data, Molecular Neurodegeneration, 4:5.doi:10.1186/1750-1326-4-5
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3262
dc.language.isoen
dc.publisherMolecular Neurodegeneration
dc.subjectprincipal component analysis (PCA)
dc.subjectPrincipal Component Analysis algorithms
dc.subjectbiological systems
dc.subjectGene microarray technology
dc.titleIndependent component analysis of Alzheimer's DNA microarray gene expression data
dc.typeArticle

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