Vivian Peng | Search PubMed

PubMed2004

Spatial patterns of gene expression in the olfactory bulb.

How olfactory sensory neurons converge on spatially invariant glomeruli in the olfactory bulb is largely unknown. In one model, olfactory sensory neurons interact with spatially restricted guidance cues in the bulb that orient and guide them to their target. Identifying differentially expressed molecules in the olfactory bulb has been extremely difficult, however, hindering a molecular analysis of convergence. Here, we describe several such genes that have been identified in a screen that compiled microarray data to create a three-dimensional model of gene expression within the mouse olfactory bulb. The expression patterns of these identified genes form the basis of a nascent spatial map of differential gene expression in the bulb.

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PubMed2002

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.

Animals↗