A bipartite graph is a powerful abstraction for modeling relationships between two collections. Visualizations of bipartite graphs allow users to understand the mutual relationships between the... Show moreA bipartite graph is a powerful abstraction for modeling relationships between two collections. Visualizations of bipartite graphs allow users to understand the mutual relationships between the elements in the two collections, e.g., by identifying clusters of similarly connected elements. However, commonly-used visual representations do not scale for the analysis of large bipartite graphs containing tens of millions of vertices, often resorting to an a-priori clustering of the sets. To address this issue, we present the Who's-Active-On-What-Visualization (WAOW-Vis) that allows for multiscale exploration of a bipartite social-network without imposing an a-priori clustering. To this end, we propose to treat a bipartite graph as a high-dimensional space and we create the WAOW-Vis adapting the multiscale dimensionality-reduction technique HSNE. The application of HSNE for bipartite graph requires several modifications that form the contributions of this work. Given the nature of the problem, a set-based similarity is proposed. For efficient and scalable computations, we use compressed bitmaps to represent sets and we present a novel space partitioning tree to efficiently compute similarities; the Sets Intersection Tree. Finally, we validate WAOW-Vis on several datasets connecting Twitter-users and -streams in different domains: news, computer science and politics. We show how WAOW-Vis is particularly effective in identifying hierarchies of communities among social-media users. Show less
Li, N.; Unen, V. van; Hollt, T.; Thompson, A.; Bergen, J. van; Pezzotti, N.; ... ; Koning, F. 2018
Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly... Show moreDeep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper. we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems. Show less
Laban, S.; Suwandi, J.S.; Unen, V. van; Pool, J.; Wesselius, J.; Hollt, T.; ... ; Roep, B.O. 2018
Auto-reactive CD8 T-cells play an important role in the destruction of pancreatic β-cells resulting in type 1 diabetes (T1D). However, the phenotype of these auto-reactive cytolytic CD8 T-cells... Show moreAuto-reactive CD8 T-cells play an important role in the destruction of pancreatic β-cells resulting in type 1 diabetes (T1D). However, the phenotype of these auto-reactive cytolytic CD8 T-cells has not yet been extensively described. We used high-dimensional mass cytometry to phenotype autoantigen- (pre-proinsulin), neoantigen- (insulin-DRIP) and virus- (cytomegalovirus) reactive CD8 T-cells in peripheral blood mononuclear cells (PBMCs) of T1D patients. A panel of 33 monoclonal antibodies was designed to further characterise these cells at the single-cell level. HLA-A2 class I tetramers were used for the detection of antigen-specific CD8 T-cells. Using a novel Hierarchical Stochastic Neighbor Embedding (HSNE) tool (implemented in Cytosplore), we identified 42 clusters within the CD8 T-cell compartment of three T1D patients and revealed profound heterogeneity between individuals, as each patient displayed a distinct cluster distribution. Single-cell analysis of pre-proinsulin, insulin-DRIP and cytomegalovirus-specific CD8 T-cells showed that the detected specificities were heterogeneous between and within patients. These findings emphasize the challenge to define the obscure nature of auto-reactive CD8 T-cells. Show less
Li, N.; Unen, V. van; Hollt, T.; Thompson, A.; Bergen, J. van; Pezzotti, N.; ... ; Koning, F. 2018
Innate lymphoid cells (ILCs) are abundant in mucosal tissues and involved in tissue homeostasis and barrier function. Although several ILC subsets have been identified, it is unknown if additional... Show moreInnate lymphoid cells (ILCs) are abundant in mucosal tissues and involved in tissue homeostasis and barrier function. Although several ILC subsets have been identified, it is unknown if additional heterogeneity exists, and their differentiation pathways remain largely unclear. We applied mass cytometry to analyze ILCs in the human fetal intestine and distinguished 34 distinct clusters through a t-SNE-based analysis. A lineage (Lin)-CD7+CD127-CD45RO+CD56+ population clustered between the CD127+ ILC and natural killer (NK) cell subsets, and expressed diverse levels of Eomes, T-bet, GATA3, and RORγt. By visualizing the dynamics of the t-SNE computation, we identified smooth phenotypic transitions from cells within the Lin-CD7+CD127-CD45RO+CD56+ cluster to both the NK cells and CD127+ ILCs, revealing potential differentiation trajectories. In functional differentiation assays, the Lin-CD7+CD127-CD45RO+CD56+CD8a- cells could develop into CD45RA+NK cells and CD127+RORγt+ ILC3-like cells. Thus, we identified a previously unknown intermediate innate subset that can differentiate into ILC3 and NK cells. Show less
PurposeAutomatically extracted coronary artery trees (CATs) from coronary computed tomography angiography imagescould contain incorrect extractions which require manual corrections before they can... Show morePurposeAutomatically extracted coronary artery trees (CATs) from coronary computed tomography angiography imagescould contain incorrect extractions which require manual corrections before they can be used in clinical practice.A model-guided method for improving the extracted CAT is described to automatically detect potential incorrectextractions and improve them.MethodsThe proposed method is a coarse-to-fine approach. A coarse improvement is first applied on all vessels in theextracted CAT, and then a fine improvement is applied only on vessels with higher clinical significance. Basedupon a decision tree, the proposed method automatically and iteratively performs improvement operations for theentire extracted CAT until it meets the stop criteria. The improvement of the extraction quality obtained by theproposed method is measured using a scoring system. 18 datasets were used to determine optimal values for theparameters involved in the model-guided method and 122 datasets were used for evaluation.ResultsCompared to the initial automatic extractions, the proposed method improves the CATs for 122 datasets from anaverage quality score of 87±6 to 93±4. The developed method is able to run within 2 minutes on a typicalworkstation. The difference in extraction quality after automatic improvement is negatively correlated with theinitial extraction quality (R=-0.694, P<0.001).ConclusionWithout deteriorating the initially extracted CATs, the presented method automatically detects incorrectextractions and improves the CATs to an average quality score of 93 guided by anatomical statistical models. Show less
Cao, Q.; Broersen, A.; Graaf, M.A. de; Kitslaar, P.H.; Yang, G.Y.; Scholte, A.J.; ... ; Dijkstra, J. 2017
