ng the dChip software. As a widely used tool for microarray data analysis, dChip can display and normalize CEL files with a model based approach. For a given group of CEL files, dChip can be used to calculate the model based expression values following website and make the qualitative detection calls for each array. The detection call provides a statistical assessment about whether the perfect matches show significantly more hybridization signal than the corresponding mis matches in a probe set. Since the detection call and expression level are computed in different ways, a gene transcript with an Absent call may still be given an expression value. from different studies, it might not be applied to an expression dataset with various tissue types.

Owing to the biological variation of gene expression across different tis sues, a baseline array should be used to normalize the microarray profiles of each tissue type. Finally, the normalized Inhibitors,Modulators,Libraries microarray profiles were inte grated Inhibitors,Modulators,Libraries into a single dataset after outlier array exclusion and global median transformation. When fitting the sta tistical model to a probe set, dChip used an outlier detec tion algorithm to identify array outliers whose response pattern for the probe set was significantly different from the consensus probe response pattern in the other arrays. After the model was fitted for all probe sets, the per centage of probe sets detected as array outliers was cal culated for each array. If the percentage exceeded 15%, the array was discarded as an outlier array. In this study, only 62 outlier arrays were detected for all the 3,030 selected expression profiles.

Inhibitors,Modulators,Libraries Global median transformation was then applied to the remaining Inhibitors,Modulators,Libraries pro files. Each expression value in a profile was divided by the profiles median value. The transformation was necessary because the expression profiles from different normalization groups often had different median values. Thus, the integrated dataset had 2,968 expression profiles with the same median value. Genome wide identification of tissue selective genes In this study, a new computational method has been designed to analyze Batimastat the integrated microarray data for identifying tissue selective genes, which refers to the genes specifically or preferentially expressed in a parti cular tissue. The computational task is not trivial for the following reasons.

First, the expression profiles have been compiled from various studies, in which tissues at different ages and in different conditions were used for microarray profiling. Thus, sellectchem the microarray profiles of the same tissue type should not be considered as biolo gical replicates. Second, some tissue selective genes can be expressed at certain developmental stages or in speci fic conditions, and their expression may not be consis tently detected in all the microarray profiles of a tissue type. Third, microarray data are inherently noisy. It was thus desired that both the expression values and detec tion calls of microarray profiles can be utilized