Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE)

Related tags

Deep LearningOG-SPACE
Overview

OG-SPACE

Introduction

Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE) is a computational framework to simulate the spatial evolution of cancer cells and the experimental procedure of bulk and Single-cell DNA-seq experiments. OG-SPACE relies on an optimized Gillespie algorithm for a large number of cells able to handle a variety of Birth-Death processes on a lattice and an efficient procedure to reconstruct the phylogenetic tree and the genotype of the sampled cells.

REQUIRED SOFTWARE AND PACKAGE

  • R (tested on version 4.0) https://cran.r-project.org
  • The following R libraries:
    • igraph
    • gtools
    • ggplot2
    • gridExtra
    • reshape2
    • stringi
    • stringr
    • shiny
    • manipulateWidget
    • rgl

RUN OG-SPACE

  • Download the folder OG-SPACE.
  • use the following command "Rscript.exe my_path\Run_OG-SPACE.R". "my_path" is the path to the folder containing the OG-SPACE scripts.
  • When the pop-up window appears, select the file "Run_OG-SPACE.R" in the working folder. Alternatively, you can launch OG-SPACE, with software like RStudio. In this case, simply run the script "Run_OG-SPACE.R" and when the pop-up window appears, select the file "Run_OG-SPACE.R" in the working folder.

PARAMETERS OF OG-SPACE

Most of the parameters of OG-SPACE could be modified by editing with a text editor the file "input/Parameters.txt". Here a brief description of each parameters.

  • simulate_process three values "contact","voter" and "h_voter". This parameter selects which model simulate with OG-SPACE.
  • generate_lattice = if 1 OG-SPACE generate a regular lattice for the dynamics. If 0 OG-SPACE takes an Igraph object named "g.Rdata" in the folder "input".
  • dimension = an integer number, the dimensionality of the generated regular lattice.
  • N_e = an integer number, number of elements of the edge of the generated regular lattice.
  • dist_interaction = an integer number, the distance of interaction between nodes of the lattice.
  • simulate_experiments = if 1 OG-SPACE generates bulk and sc-DNA seq experiments data. If 0, no.
  • do_bulk_exp = if 1 OG-SPACE generates bulk seq experiment data . If 0, no
  • do_sc_exp = if 1 OG-SPACE generates sc-DNA seq experiments data . If 0, no
  • to_do_plots_of_trees = if 1 OG-SPACE generates the plots of the trees . If 0, no.
  • do_pop_dyn_plot = if 1 OG-SPACE generates the plots of the dynamics . If 0, no.
  • do_spatial_dyn_plot = if 1 OG-SPACE generates the plots of the spatial dynamics . If 0, no.
  • do_geneaology_tree = if 1 OG-SPACE generates the plots of the cell genealogy trees . If 0, no.
  • do_phylo_tree = if 1 OG-SPACE generate the plots of the phylogenetic trees . If 0 no.
  • size_of_points_lattice = an integer number, size of the points in the plot of spatial dynamics.
  • size_of_points_trees = an integer number, size of the points in the plot of trees.
  • set_seed = the random seed of the computation.
  • Tmax = maximum time of the computation [arb. units] .
  • alpha = birth rate of the first subpopulation [1/time].
  • beta = death rate of the first subpopulation [1/time].
  • driv_mut = probability of developing a driver mutation (between 0 and 1).
  • driv_average_advantadge = average birth rate advantage per driver [1/time].
  • random_start = if 1 OG-SPACE select randomly the spatial position of the first cell . If 0 it use the variable "node_to_start" .
  • node_to_start = if random_start=0 OG-SPACE, the variable should be setted to the label of the node of starting.
  • N_starting = Number of starting cells. Works only with random_start=1.
  • n_events_saving = integer number, frequency of the number of events when saving the dynamics for the plot.
  • do_random_sampling = if 2 OG-SPACE samples randomly the cells.
  • -n_sample = integer number of the number of sampled cell. Ignored if do_random_sampling = 0
  • dist_sampling = The radius of the spatial sampled region. Ignored if do_random_sampling = 1
  • genomic_seq_length = number of bases of the genome under study.
  • neutral_mut_rate = neutral mutational rate per base [1/time].
  • n_time_sample = integer number, number of the plots of the dynamics.
  • detected_vaf_thr = VAF threshold. If a VAF is lesser than this number is considered not observed.
  • sequencing_depth_bulk = integer number, the sequencing depth of bulk sequencing.
  • prob_reads_bulk = number between 0 and 1, 1- the prob of a false negative in bulk read
  • mean_coverage_cell_sc = integer number, mean number of read per cells
  • fn_rate_sc_exp = number between 0 and 1, 1- the prob of a false negative in sc read
  • fp_rate_sc_exp = number between 0 and 1, 1- the prob of a false positive in sc read
  • minimum_reads_for_cell = integer number, the minimum number of reads per cell in order to call a mutation
  • detection_thr_sc = ratio of successful reads necessary to call a mutation

OUTPUTS OF OG-SPACE

In the folder "output", you will find all the .txt data files of the output. Note that the trees are returned as edge list matrices. The files will contain:

  • The state of the lattice, with the position of each cell.
  • The Ground Truth (GT) genotype of the sampled cells.
  • The GT Variant Allele Frequency (VAF) spectrum of the sampled cells.
  • The GT genealogy tree of the sampled cells.
  • The GT phylogenetic tree of the sampled cells.
  • The mutational tree of the driver mutations appeared during the simulation of the dynamics.
  • The genotype of the sampled cells after simulating a sc-DNA-seq experiment (if required).
  • The VAF spectrum of the sampled cells after simulating a bulk DNA-seq experiment (if required).

In the folder "output/plots", you will find all required plots.

Owner
Data and Computational Biology Group UNIMIB (was BI*oinformatics MI*lan B*icocca)
The github organization of the DCB group of the DISCo, Università degli Studi di Milano Bicocca
Data and Computational Biology Group UNIMIB (was BI*oinformatics MI*lan B*icocca)
Conversion between units used in magnetism

convmag Conversion between various units used in magnetism The conversions between base units available are: T - G : 1e4

0 Jul 15, 2021
This reporistory contains the test-dev data of the paper "xGQA: Cross-lingual Visual Question Answering".

This reporistory contains the test-dev data of the paper "xGQA: Cross-lingual Visual Question Answering".

AdapterHub 18 Dec 09, 2022
PyTorch implementation for paper "Full-Body Visual Self-Modeling of Robot Morphologies".

Full-Body Visual Self-Modeling of Robot Morphologies Boyuan Chen, Robert Kwiatkowskig, Carl Vondrick, Hod Lipson Columbia University Project Website |

Boyuan Chen 32 Jan 02, 2023
Semantic similarity computation with different state-of-the-art metrics

Semantic similarity computation with different state-of-the-art metrics Description • Installation • Usage • License Description TaxoSS is a semantic

6 Jun 22, 2022
Bayesian optimization in PyTorch

BoTorch is a library for Bayesian Optimization built on PyTorch. BoTorch is currently in beta and under active development! Why BoTorch ? BoTorch Prov

2.5k Dec 31, 2022
fklearn: Functional Machine Learning

fklearn: Functional Machine Learning fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning. Th

nubank 1.4k Dec 07, 2022
Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

SharinGAN Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation" The official project we

Koutilya PNVR 23 Oct 19, 2022
ECAENet (TensorFlow and Keras)

ECAENet: EfficientNet with Efficient Channel Attention for Plant Species Recognition (SCI:Q3) (Journal of Intelligent & Fuzzy Systems)

4 Dec 22, 2022
TDmatch is a Python library developed to perform matching tasks in three categories:

TDmatch TDmatch is a Python library developed to perform matching tasks in three categories: Text to Data which matches tuples of a table to text docu

Naser Ahmadi 5 Aug 11, 2022
Udacity Suse Cloud Native Foundations Scholarship Course Walkthrough

SUSE Cloud Native Foundations Scholarship Udacity is collaborating with SUSE, a global leader in true open source solutions, to empower developers and

Shivansh Srivastava 34 Oct 18, 2022
A Player for Kanye West's Stem Player. Sort of an emulator.

Stem Player Player Stem Player Player Usage Download the latest release here Optional: install ffmpeg, instructions here NOTE: DOES NOT ENABLE DOWNLOA

119 Dec 28, 2022
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021)

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021) Citation Please cite as: @inproceedings{liu2020understan

Sunbow Liu 22 Nov 25, 2022
This repository contains the code for our paper VDA (public in EMNLP2021 main conference)

Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models This repository contains the code for our paper VDA (publ

RUCAIBox 13 Aug 06, 2022
Official implementation of "Articulation Aware Canonical Surface Mapping"

Articulation-Aware Canonical Surface Mapping Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani Paper Project Page Requirements Python

Nilesh Kulkarni 56 Dec 16, 2022
The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

SD-AANet The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv] Overview confi

cv516Buaa 9 Nov 07, 2022
The King is Naked: on the Notion of Robustness for Natural Language Processing

the-king-is-naked: on the notion of robustness for natural language processing AAAI2022 DISCLAIMER:This repo will be updated soon with instructions on

Iperboreo_ 1 Nov 24, 2022
A Python library for generating new text from existing samples.

ReMarkov is a Python library for generating text from existing samples using Markov chains. You can use it to customize all sorts of writing from birt

8 May 17, 2022
Self-Supervised CNN-GCN Autoencoder

GCNDepth Self-Supervised CNN-GCN Autoencoder GCNDepth: Self-supervised monocular depth estimation based on graph convolutional network To be published

53 Dec 14, 2022
Code from PropMix, accepted at BMVC'21

PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels This repository is the official implementation of Hard Sample Fil

6 Dec 21, 2022