EPICC data (Heide, Househam, et al. 2022; Househam, Heide et al. 2022)
EPICC papers analysed and raw data
https://data.mendeley.com/datasets/7wx3chtsxx/2
https://ega-archive.org/studies/EGAS00001005230

EPICC analysis (Heide, Househam, et al. 2022; Househam, Heide et al. 2022)
EPICC papers data analysis pipelines
https://github.com/sottorivalab/EPICC2021_data_analysis_EPIGENOME
https://github.com/sottorivalab/EPICC2021_data_analysis_RNA

EPICC simulations and inference (Househam, Heide et al. 2022)
EPICC second paper simulation and inference
https://github.com/sottorivalab/EPICC2021_inference
https://github.com/T-Heide/MLLPT

MOBSTER (Caravagna et al. 2020)
Subclonal reconstruction in cancer by combining evolutionary theory with machine learning:
https://caravagn.github.io/mobster

MCMC-MutationalDistances (Werner et al. 2020)
Inference of de-coupled single cell microscopic parameters such as the mutation rates per division and the cell death rates from multi-sampling genomic data:
https://github.com/sottorivalab/MCMC-MutationalDistances-

CHESS (Chkhaidze et al. 2019)
A spatial model of tumour growth that also simulates different sampling strategies and the generation of genomic data:
https://github.com/sottorivalab/CHESS.cpp

REVOLVER (Caravagna et al. 2018)
Detecting repeated evolutionary trajectories in cancer using Transfer Learning:
https://github.com/sottorivalab/revolver

Quantifying-Selection (Williams et al. 2016; Williams et al. 2018)
A set of tools to measure neutral evolution and subclonal selection using either a frequentist approach or an Approximate Bayesian Computation approach for model selection:
https://marcjwilliams1.github.io/quantifying-selection