Multivariate curve resolution of time course microarray data.

ABSTRACT:

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 me

SUBMITTER: Wentzell PD 

PROVIDER: S-EPMC1539028 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature