Interactive exploration of microarray gene expression patterns in a reduced dimensional space.

ABSTRACT: The very high dimensional space of gene expression measurements obtained by DNA microarrays impedes the detection of underlying patterns in gene expression data and the identification of discriminatory genes. In this paper we show the use of projection methods such as principal components analysis (PCA) to obtain a direct link between patterns in the genes and patterns in samples. This feature is useful in the initial interactive pattern exploration of gene expression data and data-driven learning of the nature and types of samples. Using oligonucleotide microarray measurements of 40 samples from different normal human tissues, we show that distinct patterns are obtained when the genes are projected on a two-dimensional plane spanned by the loadings of the two major principal components. T

SUBMITTER: Misra J 

PROVIDER: S-EPMC186614 | biostudies-literature | 2002 Jul

REPOSITORIES: biostudies-literature