Singular value decomposition for genome-wide expression data processing and modeling

We describe the use of singular value decomposition in transforming genome-wide expression data from genes × arrays space to reduced diagonalized “eigengenes” × “eigenarrays” space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Epidermal trans-urocanic acid and the UV-A-induced photoaging of the skin

The premature photoaging of the skin is mediated by the sensitization of reactive oxygen species after absorption of ultraviolet radiation by endogenous chromophores. Yet identification of UV-A-absorbing chromophores in the skin that quantitatively account for the action spectra of the physiological responses of photoaging has remained elusive. This paper reports that the in vitro action spectrum for singlet oxygen generation after excitation of trans-urocanic acid mimics the in vivo UV-A action spectrum for the photosagging of mouse skin. The data presented provide evidence suggesting that the UV-A excitation of trans-urocanic acid initiates chemical processes that result in the photoaging of skin.