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Chin, K., DeVries, S., Fridlyand, J., Spellman, P.T., Roydasgupta, R., Kuo, W.L., Lapuk, A., Neve, R.M., Qian, Z., Ryder, T., Chen, F., Feiler, H., Tokuyasu, T., Kingsley, C., Dairkee, S., Meng, Z., Chew, K., Pinkel, D., Jain, A., Ljung, B.M., Esseman, L., Albertson, D.G., Waldman, F.M. and Gray, J.W. (2006) Genomic and Transcriptional Aberrations Linked to Breast Cancer Pathophysiologies. Cancer Cell, 10, 529-541.
https://doi.org/10.1016/j.ccr.2006.10.009

has been cited by the following article:

• TITLE: Microarray Analysis Using Rank Order Statistics for ARCH Residual Empirical Process

  AUTHORS: Hiroko Kato Solvang, Masanobu Taniguchi

  KEYWORDS: Time Series Model, ARCH, Wilcoxon Statistic, Volatility, Deferentially Expressed Gene Signatures, Two-Group Comparison, Breast Cancer GEO, Genome-Wide Expression Profiling, GO Analysis

  JOURNAL NAME: Open Journal of Statistics, Vol.7 No.1, February 20, 2017

  ABSTRACT: Statistical two-group comparisons are widely used to identify the significant differentially expressed (DE) signatures against a therapy response for microarray data analysis. We applied a rank order statistics based on an Autoregressive Conditional Heteroskedasticity (ARCH) residual empirical process to DE analysis. This approach was considered for simulation data and publicly available datasets, and was compared with two-group comparison by original data and Auto-regressive (AR) residual. The significant DE genes by the ARCH and AR residuals were reduced by about 20% - 30% to these genes by the original data. Almost 100% of the genes by ARCH are covered by the genes by the original data unlike the genes by AR residuals. GO enrichment and Pathway analyses indicate the consistent biological characteristics between genes by ARCH residuals and original data. ARCH residuals array data might contribute to refining the number of significant DE genes to detect the biological feature as well as ordinal microarray data.