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[Analysis of high-dimensional biomarker data is in general of exploratory nature and aims to discover or dissect subgroups of patients sharing a specific pattern of biomarker measurements. One major challenge is to extract the relevant markers from the extremely large pool of measured markers. Specific techniques such as grouping and ordering and dimension reduction allow to aggregate huge amounts of data into single meaningful graphics. These graphics can guide the direction of exploration during the analysis. We present graphical tools for unsupervised and supervised objectives based on gene expression data of multiple myeloma patients which are part of the MAQC-II project.]
Published: Sep 28, 2012
Keywords: High-dimensional; Biomarker; Genomic
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