Sharp Visualization

Overview first – detail on demand – insight in sight

Scientific problems are rarely answered by direct comparison between A and B; next, we want to learn about the impact of parameter 1, 2, and 3 and further, stratify our patient groups according to shared properties and differences observed in our data. These are constructive and necessary steps when complex phenomena are the objective of the studies, e.g. a high volume of multivariate biomedical data are analyzed to give information about cause and diagnosis of a disease, an individual‘s prognosis and potentially, the responsiveness to a special medication.
As expected, complex phenomena are mirrored by the complexity of the respective multi-scale, highly interconnected and condition-dependent data. It is a real challenge to visually inspect all relevant data, decide which level of simplification is suitable to clearly reveal patterns in the data and points towards new insights. Especially, multidimensional data patterns are difficult to recognize when encoded into two dimensions. Therefore, the real artistry lies in the extraction of relevant information by applicable analytical methods and the explorative visualization of the results.

” I simply can’t figure it out..“

Interactive visualizations for intuitive data exploration

We develop beautiful visualizations that make life science data fun to explore – interactively and intuitively. Inspect your data from the perspective that you define – by including information from connected data types and dynamically enriching plots with associated metadata. Interactively configure downstream analysis on subpopulations of your data with a few clicks – stratify, analyze, advance!

Showcase

Exploring complex data with sharp visualization: The video demonstrates use of Genevention’s PathoDB – a comprehensive knowledge base of potentially pathogenic organisms found in transcriptomics datasets. An interactive heat map gives an overview which organisms have been found enriched in experimental datasets associated with diseases – overlaid by associations extracted from the literature. Interactively explore infection signatures of different patients in the context of connected metadata: age, gender, disease information – even results from analyses can be integrated seamlessly and dynamically. Selecting patient’s data points with a “lasso” allows to investigate the distribution of confounding factors of a group – cluster analysis made easy and fun! Stratify your cohort and perform downstream analyses on subpopulations of your interest, explore results and their meaning – just by a few clicks.