A mixed model approach to IC50 estimation that enables simultaneous estimation of IC50 values across the entire set of cell lines and compounds. We show that this approach improves the accuracy of the estimates and significantly reduces the compute time.
A two-stage penalized linear regression approach that uses upstream (genomics) and downstream (transcriptomics) to predict drug response. It results in models that are more interpretable but still achieves comparable predictive performance rate.
Identifies co-occurrence and mutual exclusivity in somatic mutations using an elegant analytical null-model, which we show to faithfully recapitulate the nominal rates. The method suggests that many of the reported co-occurrences are in fact expected based on chance alone.
Prioritizes oncogenes and tumor suppressor genes based on the integration of various molecular data types.
Pinpoint driver genes in focal recurrent aberrations (across tumor samples) in DNA somatic copy number data.
A software package with samplers for Bayesian inference of computational models
BRCA1-like and BRCA2-like shrunken centroid classifiers. R package and documentation here.
Matlab code for detecting recurrent aberrations in DNA copy number data.
R code for Kernel Convolved Rule Based Mapping can found here.
Data and scripts can be found here.
- The R ‘optimalCaptureSegmentation’ package
Please note that this is a preview version, that will be published to CRAN or BioConductor once the documentation is complete, and some other loose ends are tied up.
Download the R package: optimalCaptureSegmentation_0.9-4.tar.gz.
NOTE: please read the help-page on function ‘findOptimalSegmentations()’ after loading the package. In particular, it is advised to study the Example section, which should explain quite clearly what the findOptimalSegmentations() function is actually doing.
NOTE: Version 0.9-4 fixes a bug where the ‘alphaEstimated’ value for segmentation solutions was calculated incorrectly.
- MASDA: R-code for Mass Spectrometry Data Analysis
Download version 0.6 of the package here (Linux) or here (Windows). Within R, after installation, type ?MASDA for some (very limited) documentation. For questions/suggestions/remarks, please send e-mail to firstname.lastname@example.org
- BicBin: Biclustering Sparse Binary Genomic Data
Download the MATLAB and R code here. A full-colour version of Figure 8 can be found here. Supplementary information accompanying the paper (submitted) can be found here. For questions/suggestions/remarks, please send e-mail to email@example.com
- EPICURE code for fitting latency models using B-splines
More information here.
- SIRAC code for analysis of aCGH data
Download the MATLAB code here. A description of the content of the code is given here. For questions/suggestions/remarks, please send e-mail to firstname.lastname@example.org
- Software package for cooccurrence analysis of insertional mutagenesis data
MATLAB code can be found here, the manual can be found here.
KC-SMART R package.
This package is a part of the Bioconductor bioinformatics toolbox for the statistical analysis software R. This is the most up to date version.
KC-SMART program package.
This package requires the Matlab runtime component available from the Mathworks website. This version of the software is very old and not maintained anymore.
Common Insertion Site Mapping Platform for statistical analysis of retroviral insertional mutagenesis screens.
Matlab: Package and manual. R: GitHub repository.