Background adjustment of cDNA microarray images by Maximum Entropy distributions

Background adjustment of cDNA microarray images by Maximum Entropy distributions

Many empirical studies have demonstrated the exquisite sensitivity of both traditional and novel statistical and machine intelligence algorithms to the method of background adjustment used to analyze microarray datasets. In this paper we develop a statistical framework that approaches background adjustment as a classic stochastic inverse problem, whose noise characteristics are given in terms o...

cDNA Microarray Data Normalization

Analysis of CDNA Microarray Images

Microarrays are part of a new class of biotechnologies that allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one that can have a potentially large impact on subsequent analyses, such as clustering or the identification of differentially expressed genes. This paper reviews a number of existing image ...

Microarray BASICA: background adjustment, segmentation, image compression and analysis of microarray images

This paper presents microarray BASICA: an integrated image processing tool for background adjustment, segmentation, image compression, and analysis of cDNAmicroarray images. BASICA uses a fast Mann-Whitney test-based algorithm to segment cDNA microarray images and performs postprocessing to eliminate the segmentation irregularities. The segmentation results, along with the foreground and backgr...

Maximum entropy distributions on graphs

Inspired by the problem of sensory coding in neuroscience, we study the maximum entropy distribution on weighted graphs with a given expected degree sequence. This distribution on graphs is characterized by independent edge weights parameterized by vertex potentials at each node. Using the general theory of exponential family distributions, we prove the existence and uniqueness of the maximum l...