Normalization of microarray data using a spatial mixed model analysis which includes splines

Normalization of microarray data using a spatial mixed model analysis which includes splines

MOTIVATION Microarray experiments with thousands of genes on a slide and multiple slides used in any experimental set represent a large body of data with many sources of variation. The identification of such sources of variation within microarray experimental sets is critical for correct deciphering of desired gene expression differences. RESULTS We describe new methods for the normalization ...

cDNA Microarray Data Normalization

Normalization of dye bias in microarray data using the mixture of splines model.

This paper discusses characteristics of dye biases in microarray data that the conventional normalization methods do not handle, and proposes a new normalization method involving a mixture of splines model. We also develop a test for between-group comparisons of each gene that is designed to be used with our proposed method.

Normalization of Microarray Data

DNA microarrays are a powerful technology for analysis of gene expression levels within cells. Both the cDNA and oligonucleotide technologies for microarrays are fairly young and are prone to a broad collection of errors and inaccuracies [KYML02]. In this paper we briefly discuss some sources of error and detail a variety of methods for normalizing the data with respect to some of these error s...

Normalization of cDNA microarray data.

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a wel...