Sparse Canonical Correlation Analysis : Data Integration for regular and high dimensional studies

ISBN-13: 9783639158885 / Angielski / Miękka / 2009 / 152 str.

Elena Parkhomenko

Opis

Sparse Canonical Correlation Analysis (SCCA) performs data integration by simultaneous analysis of 2 data types to find the relationships between them. It is applicable to all studies including large studies with limited sample size. SCCA provides sparse solutions that include only a small subset of variables. Sparse results aid in biological interpretability and can be used for hypothesis generation. This monograph presents methodology for SCCA and evaluation of its properties using simulated data. The practical use of SCCA is illustrated by applying it to the study of natural variation in human gene expression. Two extensions of SCCA - adaptive SCCA and modified adaptive SCCA are also presented. Their performance is evaluated and compared using simulated data and adaptive SCCA is applied to the study of natural variation in human gene expression.

O autorze

Dr. Elena Parkhomenko received a PhD in Biostatistics at the
University of Toronto. Her research interests are in the area
of data integration in the context of high-dimensional studies
and discovery of modest effects in multiple-testing problems.