We specialize in automated solutions for exporting, transforming, and consolidating data into a unified database, enhancing streamlined visualization and data interpretation. The amalgamation of raw data from various devices serves as a potent analytical resource, enabling comparisons between similar devices from different manufacturers or uncovering insights from distinct assays that would otherwise require laborious individual assessments.
While most experiments involve fitting to a single defined model, certain experiments like SPR single-cycle kinetics require the use of changing models across data series. While many modern software solutions have implemented this, flexibility is still limited in terms of weighting the influence of each injection phase during fit optimization and allowing the selection of individual models for each phase. Our R-packages offer even more flexibility, allowing for graphical representation of multiple models and automatic visualization of large datasets, enabling swift identification of the best binder at a glance.
By combining Windows and Linux command line, Matlab, CellProfiler and R programming language, we were able to create a semi-automated pipeline to perform image transformation (flat- and darkfielding), segmentation (parallel cell tracking at multiple positions), extraction of data on the single-cell level, as well as subsequent data fitting on single timelines.
Utilizing the capabilities of ShinyR, we can craft interactive apps for exploratory data analysis. Here, we introduce an exemplary app that enables you to estimate the time needed to reach your financial retirement goal.
Specialized data presentation scripts enable the creation of not only beautiful plots but also streamline the data analysis process, reducing both time and effort. Our mass spectrometry package facilitates peak detection and automatic identification of mass differences. What sets our scripts apart is their capability to automatically assess and annotate combinations of possible mass differences.