Two training sets were collected for the cell line dataset. Cell lines used in this dataset include mouse and human fibroblast cells, human breast cancer cells, kidney cells and prostate cancer cells. A wide variety of molecular targets in the cell nuclei (H3K27me3, H3K4me3, DNA, RNA polymerase II) were labeled, which are characteristic of a diverse “texture” of nuclear organization. These datasets were generally divided into two categories based on its image characteristics: discrete and dense nuclear texture, which depend on either molecular targets or biological states. A total of 44 STORM images with discrete nuclear texture and an additional 65 images with dense nuclear texture were used in this study. Each dataset was divided: 60% for training, 30% for testing and 10% for validation. The STORM images of cell nuclei with discrete nuclear texture comprised our first, single-source (single labeled target of RNA polymerase II) cell line training and validation dataset (Additional file 9: Figure S8), herein referred to as cell line dataset A. Next, the STORM image training and validation datasets including images with both discrete and dense nuclear texture comprised our second, multiple-source (i.e., various labeled targets) training and validation set, which we shall call cell line dataset B. Then, the two test sets from cell line datasets A & B were combined to create a single test dataset for both the discrete and dense nuclear texture cell line training sets. In this way, we directly compared the network performances of both models: trained on a single data source presenting a discrete texture, versus trained on multiple sources containing both discrete and dense texture. A more complete breakdown of cell type, biological state, labeled molecular target and fluorophore used in each image set, as well as a breakdown of which dataset each image type appears in (Additional file 8: Table S1).

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