The above scripts from Bisognin et al. were used by an additional script to calculate the number of genomic clusters for 1000 randomly generated lists of 675 probesets. The mean number of genomic clusters was 3.4, with a range of 0 to 9 clusters. The distance from the edge of a chromosome territory to the edge of the nucleus was calculated using a macro in ImageJ. Prior to image analysis, all images were deconvolved using the Huygens software package. Subsequent image processing and distance mapping were done with a macro written for ImageJ. All chromosome territory images were subjected to a despeckle followed by thresholding and creation of a filled binary image mask. The territory mask was then mapped onto a Euclidean Distance map of the DAPI signal for each slice. The LY2109761 minimum pixel intensity value of the DAPI EDM enclosed by the chromosome territory mask was recorded for each slice. The lowest pixel value was taken as the shortest distance from the edge of the chromosome territory to the edge of the nucleus in x-y. The image stack was then re-sliced perpendicular to the x-y axis and the process was repeated for each x-z slice. The minimum distance to the edge of the DAPI signal was again calculated for each particle. The minimum value for distance for the x-y or x-z axes was scored as the shortest distance from the edge of a territory to the edge of the nucleus for each chromosome territory in a nucleus. Volumes of chromosome territories and total nuclear volumes were calculated using surfaces feature of Imaris Software version 7.1.1. Serial analysis of gene expression can quantitatively evaluate expression profiles of the entire Epoxomicin transcriptome without prior sequence information in contrast to the microarrays. SAGE provides high sensitivity for mRNAs of low abundance and detects slight differences in expression levels between samples, providing information necessary for the identification of new tumor biomarkers and suppressors. SAGE usually generates a huge amount of experimental data, i.e., SAGE tag sequences and their counts. It is necessary to extract and arrange the relevant information in SAGE data to find a key SAGE tag. Many publicly available bioinformatics tools were developed to address this point. However, they fail to provide the cross-tissue comparison of gene expressions, which means that the mined SAGE tag sequences representing the tumor marker candidates in some tissues can not simultaneously be cross-compared to the tumor marker candidates in other tissues. Moreover, matrix data is usually not provided in SAGE.