Fig. 1 | Nature Communications

Fig. 1

From: Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types

Fig. 1

Schematic overview of Cytosplore+HSNE for exploring the mass cytometry data. By creating a multi-level hierarchy of an illustrative 3D data set (a), we achieve a clear separation of different cell groups in an overview embedding (left panel b) that conserves non-linear relationships (i.e., follows the distance indicated by the dashed line in a, instead of the grey arrow) and more detail within the separate groups on the data level (right panel b). c Construction and exploration of the hierarchy. The hierarchy is constructed starting with the data level (left two columns). On the basis of the high-dimensional expression patterns of the cells, a weighted kNN graph is constructed, which is used to find representative cells used as landmarks in the next coarser level. By administering the area of influence (AoI) of the landmarks, cells/landmarks can be aggregated without losing the global structure of the underlying data or creating shortcuts. The exploration of the hierarchy is shown in the two rightmost columns. At the bottom, we see the overview level (in this example the 3rd level in the hierarchy), which shows that a group of landmarks has low expression in marker c (bottom-right panel). Selecting this group of landmarks for further exploration results in a look-up of the landmarks in the preceding level (neighborhood graph, intermediate level) that are in the AoI, with which a new embedding can be created at the 2nd level of the hierarchy (middle-right panel). Marker b shows a strong separation between the upper and lower landmarks at this level. Zooming-in on the landmarks with low expression of marker b reveals further separation in marker a at the lowest level, the full data level (top-right panel)

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