Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators | Data acquisition and preparation | Effects of stimulation on B and T cells | Collecting single cell data | CD8^+^ T and IgM^+^ B cells profile | Phenotypic | Functional | Selecting channels relevant to T cells | Using Radviz to visualize the effects of stimulation | Using Freeviz to visualize the effects of stimulation | Building a functional T cells graph | Building a cell-level distance matrix | Extracting an adjacency matrix | Computing edge weights from adjacency matrix | Building a kNN graph | Extracting communities as functional states | From communities to functional states | Using Graphviz to visualize functional graph

Abstract | Introduction | Building a Radviz projection | Installation | Visualizing High Dimensional Data: the bodenmiller dataset | Normalizing the data | Defining the anchors | Optimizing the position of anchors | Projection | Visualizing the results | Visualizing Signal Intensity for Functional Channels | Visualizing Cell Populations | Visualizing Functional Changes

Abstract | Introduction | Building a Fan plot | Installation | Visualizing mass cytometry data from the bodenmiller package | Visualizing functional differences after stimulation. | Conclusion

Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators | Data acquisition and preparation | The Reference dataset | The Untreated dataset

Introduction | Installation | Loading some test data | Defining a grid to compare samples | Choosing a number of bins | Reviewing the grid | Applying the grid to the data | Ordering the dimensions | Calculating the Hilbert index | Visualizing the Hilbert curve | Using the Andrews curve to visualize cell densities | Projecting the Hilbert curve in 2 Dimensions | Comparing cells using Hilbert index

Introduction | Installation | Loading some test data | Computing the similarity between treatments | Identifying treatment effects