The following research tools are used for general data analysis, proteomics and clinical trial statistics
Sustainable materials form the backbone for a transition towards a sustainable and healthy society. The research topcis include, but are not limited to, organic optoelectronics, wide bandgap semiconductors, printing & coating technology, polymer upcycling technology, innovative & smart packaging, valorization schemes for biomass, and advanced material characterization. On this page you can find out more about the aforementioned topics.
This tool aids in the identification of global and local structures in complex datasets.
Keywords: topological data analysis, visual analytics
Type: R package, python library
More information at http://vda-lab.be/stad.html
Installation: pip install stad
Contact: Jan Aerts
QCQuan analyzes your labeled LC-MS/MS proteomics differential expression experiment and provides you with (normalized) output files on both the non-redundant-peptide level as well as protein level, including a quality control and differential expression report in PDF format.
Keywords: proteomics workflow; mass spectrometry; differential expression; quality control; normalization
More information at https://qcquan.net/
Reference: J. Proteome Res. 2019, 18, 5, 2221-2227
Contact: Joris Van Houtven
Methods that predict the monoisotopic mass based on the average mass are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models that allows prediction of the monoisotopic mass based on the exact mass of the most-abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes
Type: R shiny app
More information at https://valkenborg-lab.shinyapps.io/mind/
Contact: Dirk Valkenborg
BRAIN = Baffling Recursive Algorithm for Isotope distributioN calculations. This package calculates aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg.
Type: R Bioconductor
More information at: https://bioconductor.org/packages/release/bioc/html/BRAIN.html
Reference: Anal. Chem. 2013, 85, 4, 1991-1994
Contact: Dirk Valkenborg
The R package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks.
Keywords: clinical trials; endpoints
More information at https://cran.r-project.org/web/packages/Surrogate/
Reference: Alonso, A., Bigirumurame, T., Burzykowski, T., et al. (2017). Applied Surrogate Endpoint Evaluation with SAS and R. Boca Raton: Chapman&Hall/CRC
Contact: Geert Molenberghs