Knowledge of protein subcellular localisation assists in the elucidation of protein function and understanding of different biological mechanisms which occur at discrete subcellular niches. Organelle-centric proteomics enables localisation of thousands of proteins simultaneously. Although such techniques have succesfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localisation based on co-migration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled with sophisticated computational tools. Here, we apply and compare multiple approaches to establish a high confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immuno isolations of the TGN, coupled with probability based organelle proteomics techniques. Specifically the technique known as LOPIT (Localisation of Organelle Protein by Isotope Tagging), couples density centrifugation with quantitative mass spectometry based proteomics using isobaric labelling and targeted methods with semi-supervised machine learning methods. We demonstrate that whilst the immuno isolation method gives rise to a significant dataset, the approach is unable to distinguish cargo proteins and persistant contaminants from full time residents of the TGN. The LOPIT approach however, returns information about many subcellular niches simultaneously and the steady state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady state location favours the TGN. Using this approach we present a robust list of Arabidopsis TGN proteins.