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MIDAA

Multiomics Integration via Deep Archetypal Analysis (MIDAA)

MIDAA is a package designed for performing Deep Archetypal Analysis on multiomics data.

Reference: Milite 2024, biorxiv

Code: https://github.com/sottorivalab/midaa

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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.

Reference: Kouadri Boudjelthia, 2024, biorxiv

https://github.com/idriskb/NeuroVelo

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EPICC ANALYSIS

EPICC papers data analysis pipelines

EPICC analysis (Heide, Househam, et al. 2022; Househam, Heide et al. 2022)

https://github.com/sottorivalab/EPICC2021_data_analysis_EPIGENOME

https://github.com/sottorivalab/EPICC2021_data_analysis_RNA

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EPICC SIMULATIONS AND INFERENCE

EPICC simulations and inference

EPICC second paper simulation and inference

Reference: Househam, Heide et al. 2022

https://github.com/sottorivalab/EPICC2021_inference

https://github.com/T-Heide/MLLPT

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MOBSTER

Subclonal reconstruction in cancer by combining evolutionary theory with machine learning

Reference: Caravagna et al. 2020

Code: https://github.com/caravagnalab/mobster

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MCMC - MUTATIONAL DISTANCES

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

Reference: Werner et al. 2020

Code: https://github.com/sottorivalab/MCMC-MutationalDistances-

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CHESS

A spatial model of tumour growth that also simulates different sampling strategies and the generation of genomic data:

Reference: CHESS Chkhaidze et al. 2019

Code: https://github.com/sottorivalab/CHESS.cpp

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REVOLVER

Detecting repeated evolutionary trajectories in cancer using Transfer Learning:

Reference: Caravagna et al. 2018

Code: https://github.com/sottorivalab/revolver

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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

Reference: Quantifying-Selection (Williams et al. 2016; Williams et al. 2018)

Code: https://marcjwilliams1.github.io/quantifying-selection

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