Multivariate curve resolution of time course microarray data

Background: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. Results: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Peter D. Wentzell, Tobias K. Karakach, Sushmita Roy, M. Juanita Martinez, Christopher P. Allen, Margaret Werner-Washburne
• Keywords: BMCBI 2006, Expression Data, Gene Expression Data, Measurement Error