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Integrated Analysis of Gene Expression and Copy Number Data on Gene Shaving Using Independent Component Analysis

Integrated Analysis of Gene Expression and Copy Number Data on Gene Shaving Using Independent Component Analysis,10.1109/TCBB.2011.71,IEEE/ACM Transac

Integrated Analysis of Gene Expression and Copy Number Data on Gene Shaving Using Independent Component Analysis   (Citations: 2)
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DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving "( ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer. Index Terms—Clustering technique, comparative genomic hybridization (CGH), copy number variation (CNV), generalized singular value decomposition (GSVD), gene expression, gene shaving, independent component analysis (ICA). Ç
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    • ...The algorithms were implemented in Matlab and the codes and data are available for download on the website [37]...

    Jinhua Shenget al. Integrated Analysis of Gene Expression and Copy Number Data on Gene Sh...

    • ...Using ICA we are able to locate genes that have correlated patterns as well as the dissimilar patterns in both gene expression and aCGH data [10]...
    • ...statistically independent and non-Gaussian [9,10, 11]...
    • ...Several often used ICA algorithms are the FastICA[10], Infomax and joint approximate diagonalization of eigen-matrices (JADE) [11]...
    • ...Copy number data were generated using the model proposed by Wang et al [6] and gene expression data were generated based on the model of Attoor et al [10]...
    • ...The relation between copy number and gene expression states was modeled using a simple state flow [10]...
    • ...In order to evaluate the robustness of the method to noise, the gene list percentage similarity (PS) was computed by counting the number of genes obtained from noisy data (ND) intersecting with that obtained from the original data (OD) [10]...
    • ...ICA finds the statistically independent components, and is more suitable for separating mixed signals [10]...
    • ...More details are reported in our paper [10]...

    Yu-Ping Wang. Integrated Analysis of Gene Expression and Gene Copy Number for Gene S...

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