BaalChIP corrects for the effect of background allele frequency on the observed ChIP-seq read counts jointly analyses multiple ChIP-seq samples across a single variant.
A probabilistic framework for reconstructing intra-tumor phylogenies. In a full Bayesian approach, we jointly estimate the number and composition of clones as well as the most likely tree connecting them.
Image analysis of histopathologically stained tumour slides.
Coming soon! DANCE quantifies the impact of copy-number alterations on gene expression and compares it between tumour sub-types.
Analysis of IFISH (Immunofluorescence + Fluorescence in situ Hybridisation) images, performing nuclear, membrane and spot detection in order to quantify heterogeneity at the single cell level.
The one stop shop for enrichment and network analyses of high-through- put RNAi screens. Turns cellHTS2 output into annotated tables and graphs.
Various optimization methods for matrix-to-matrix Lasso inference: cross-validation, randomised lasso, subsampling lasso and others.
MEDICC harnesses the power of a finite-state automaton representation of genomic profiles to model genomic rearrangement events with horizontal dependencies.
NEMs reconstruct features of pathways from the nested structure of perturbation effects. The package unites the software from several labs and represents the current state-of-the-art in Nested Effects Models.
OncoNEM is a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. Built on NEM technology!
The package implements methods to encode posteriori belief of functional association types on edges and phenotypic information on nodes.
Network Data Integration, Analysis, and Visualization really in a Box. There is no better way to work with networks in R!
Reconstruction of transcriptional networks including Master Regulator Analysis.
SANTA functionally annotates networks like standard enrichment methods annotate lists of genes.
Software hosted by collaborators
Richard Savage hosts a page to supplement our paper 'Patient-specific data fusion defines prognostic cancer subtypes' from where you can download MATLAB code implementing our clustering method.
Cloe (pronounced like the name Chloë) is a computational biology tool to infer the clonal structure of heterogeneous tumour samples. It implements a phylogenetic latent feature model which discovers hierarchically related patterns (clonal genotypes) in the samples, and with these describes the observed mutation data.