OncoSimulR - Forward Genetic Simulation of Cancer Progression with Epistasis

Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness (including just birth, just death, or both birth and death) can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions, including adaptive therapy, is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them.

Last updated 3 months ago

biologicalquestionsomaticmutationcpp

6.92 score 6 stars 66 scripts 276 downloads

varSelRF - Variable Selection using Random Forests

Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications).

Last updated 8 years ago

6.48 score 12 stars 2 dependents 83 scripts 538 downloads

ADaCGH2 - Analysis of big data from aCGH experiments using parallel computing and ff objects

Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data.

Last updated 3 months ago

microarraycopynumbervariants

3.48 score 3 scripts 440 downloads

PHYLOGR - Functions for Phylogenetically Based Statistical Analyses

Manipulation and analysis of phylogenetically simulated data sets and phylogenetically based analyses using GLS.

Last updated 5 years ago

3.02 score 26 scripts 233 downloads