Nuclear organization of chromatin is an important level of genome regulation with positional changes of genes occurring during reprogramming. Inherent variability of biological specimens, wide variety of sample preparation and imaging conditions, though pose significant challenges to data analysis and comparison. Here, we describe the development of a computational image analysis toolbox overcoming biological variability hurdles by a novel single cell randomizing normalization. We performed a comparative analysis of the relationship between spatial positioning of pluripotency genes with their genomic activity and determined the degree of similarity between fibroblasts, induced pluripotent stem cells and embryonic stem cells. Our analysis revealed a preferred positioning of actively transcribed Sox2, Oct4 and Nanog away from the nuclear periphery, but not from pericentric heterochromatin. Moreover, in the silent state, we found no common nuclear localization for any of the genes. Our results suggest that the surrounding gene density hinders relocation from an internal nuclear position. Altogether, our data do not support the hypothesis that the nuclear periphery acts as a general transcriptional silencer, rather suggesting that internal nuclear localization is compatible with expression in pluripotent cells but not sufficient for expression in mouse embryonic fibroblasts. Thus, our computational approach enables comparative analysis of topological relationships in spite of stark morphological variability typical of biological data sets.