High-resolution spatial normalization for microarrays containing embedded technical replicates.

ABSTRACT:

Motivation

Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited.

Results

We introduce a new approach for analyzing spatial artifacts, termed 'conditional residual analysis for microarrays' (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal s

SUBMITTER: Yuan DS 

PROVIDER: S-EPMC2262854 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature