MIDAA
Multiomics Integration via Deep Archetypal Analysis (MIDAA)
MIDAA is a package designed for performing Deep Archetypal Analysis on multiomics data.
https://github.com/sottorivalab/midaa
MIDAA is a package designed for performing Deep Archetypal Analysis on multiomics data.
https://github.com/sottorivalab/midaa
NEUROVELO
NeuroVelo: interpretable learning of cellular dynamics
NeuroVelo: physics-based interpretable learning of cellular dynamics. It is implemented on Python3 and PyTorch, the model estimate velocity field and genes that drives the splicing dynamics.
https://github.com/idriskb/NeuroVelo
NeuroVelo: physics-based interpretable learning of cellular dynamics. It is implemented on Python3 and PyTorch, the model estimate velocity field and genes that drives the splicing dynamics.
https://github.com/idriskb/NeuroVelo
EPICC ANALYSIS
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 papers data analysis pipelines
https://github.com/sottorivalab/EPICC2021_data_analysis_EPIGENOME
https://github.com/sottorivalab/EPICC2021_data_analysis_RNA
EPICC SIMULATIONS AND INFERENCE
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
EPICC second paper simulation and inference
https://github.com/sottorivalab/EPICC2021_inference
https://github.com/T-Heide/MLLPT
MOBSTER
MOBSTER (Caravagna et al. 2020)
Subclonal reconstruction in cancer by combining evolutionary theory with machine learning:
https://caravagn.github.io/mobster
Subclonal reconstruction in cancer by combining evolutionary theory with machine learning:
https://caravagn.github.io/mobster
MCMC - MUTATIONAL DISTANCES
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-
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
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
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
REVOLVER (Caravagna et al. 2018)
Detecting repeated evolutionary trajectories in cancer using Transfer Learning:
https://github.com/sottorivalab/revolver
Detecting repeated evolutionary trajectories in cancer using Transfer Learning:
https://github.com/sottorivalab/revolver
QUANTIFYING-SELECTION
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
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