Comparing single cell datasets using DensityMorph

The proliferation of single cell datasets has brought a wealth of information, but also great challenges in data analysis. Obtaining a cohesive overview of multiple single cell samples is difficult and requires consideration of cell population structure - which may or may not be well defined - along with subtle shifts in expression within cell populations across samples, and changes in population frequency across samples. Ideally, all this would be integrated with the experimental design, e.g. time point, genotype, treatment etc. Data visualisation is the most effective way of communicating analysis but often this takes the form of a plethora of t-SNE plots, colour coded according to marker and sample. In this manuscript, I introduce a novel exploratory data analysis and visualisation method that is centred around a novel quasi-distance (DensityMorph) between single cell samples. DensityMorph makes it possible to plot single cell samples in a manner analogous to performing principal component analysis on microarray samples. Biological interpretation is ensured by the introduction of Explanatory Components, which show how marker expression and coexpression drive the differences between samples. This method is a breakthrough in terms of displaying the most pertinent biological changes across single cell samples in a compact plot. Finally, it can be used either as a stand-alone method or to structure other types of analysis such as manual flow cytometry gating or cell population clustering.

Visualising user–website interaction: description and evaluation of a teaching method

This paper presents a teaching method applied in a usability research course that is part of a bachelor degree programme at the Academy of Fine Arts in Katowice. The method employs visualisation techniques of the user–website interaction, a design practice popular in other fields, but less often used in usability studies. The theoretical background of the data visualisation method, as well as examples of its use in research, are presented and discussed in this paper. In addition to presenting the method, the paper evaluates and analyses how students have responded to it. Using the technology acceptance model, we identified the perceived usability of the method as the main factor influencing students’ behavioural intention to use it in the future.

Visualising Patterns Associated with Adverse Drug Reactions in French Forums

As social media are an interesting source of information for pharmacovigilance, we implemented a novel visualisation method for pharmacovigilance specialists applied to French discussion forums. A word embedding model was trained on posts to facilitate the identification of patterns associated with adverse drug reactions.