cancer-evolution
There are 1 repository under cancer-evolution topic.
caravagnalab / revolver
REVOLVER - Repeated Evolution in Cancer
Language:R 64- Language:R 24
reiterlab / ctdna
Python package for cancer early detection based on a model of cancer evolution and circulating tumor DNA (ctDNA) shedding
Language:Python 10rdiaz02 / what_genotype_next
Code for "Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?", J.Diaz-Colunga, R.Diaz-Uriarte: https://doi.org/10.1371/journal.pcbi.1009055
Language:R 7BIMIB-DISCo / J-Space.jl
J-SPACE is a Julia package to simulate the spatial growth and the genomic evolution of a cell population and the experiment of sequencing the genome of the sampled cells.
Language:Julia 6caravagn / evoverse.datasets
Datasets and analysis results released with the REVOLVER package for Cancer Evolution.
Language:R 5aleighbrown / pwgs_snakemake
Snakemake pipeline for running PhyloWGS on NIH Biowulf Cluster
Language:Python 4Clone trees for Cancer Evolution studies from bulk sequencing data.
Language:R 4The evoverse is a package to implement cancer evolution analysis on multi-sample cancer sequecing data.
Language:HTML 3- Language:Python 3
TomMakesThings / Cancer-Evolution
Estimates the clonal population structure in a tumour sample given a cell mutation matrix
Language:Jupyter Notebook 3- Language:Python 2
- mtreeLanguage:R 2
iamfuxiao / tumour-growth-patterns-impact-evolution
Agent-based modelling reveals the impact of growth patterns on spatial and temporal features of clonal diversification. A GitHub repository of the Source Code for the model and Source Data for the figures of the paper.
Language:Jupyter Notebook 2FrancisCrickInstitute / tumour-growth-patterns-impact-evolution
Agent-based modelling reveals the impact of growth patterns on spatial and temporal features of clonal diversification. A GitHub repository of the Source Code for the model and Source Data for the figures of the paper.
Language:Cuda 1- Language:C 0
Supplementary Data released with Caravagna et al. Subclonal reconstruction of tumors by using machine learning and population genetics. Nature Genetics volume 52, 898–907(2020).
Language:HTMLAnalysis pipleine to model tumour clonal evolution from WGS data (driver annotation, quality control of copy number calls, subclonal and mutational signature deconvolution)
