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)
Py-faster-rcnn - Faster R-CNN (Python implementation)

py-faster-rcnn has been deprecated. Please see Detectron, which includes an implementation of Mask R-CNN. Disclaimer The official Faster R-CNN code (w

Ross Girshick 7.8k Jan 03, 2023
Ascend your Jupyter Notebook usage

Jupyter Ascending Sync Jupyter Notebooks from any editor About Jupyter Ascending lets you edit Jupyter notebooks from your favorite editor, then insta

Untitled AI 254 Jan 08, 2023
Real-time Neural Representation Fusion for Robust Volumetric Mapping

NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping Paper | Supplementary This repository contains the implementation of

ETHZ ASL 106 Dec 24, 2022
LiDAR R-CNN: An Efficient and Universal 3D Object Detector

LiDAR R-CNN: An Efficient and Universal 3D Object Detector Introduction This is the official code of LiDAR R-CNN: An Efficient and Universal 3D Object

TuSimple 295 Jan 05, 2023
YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

Introduction Yolov5-face is a real-time,high accuracy face detection. Performance Single Scale Inference on VGA resolution(max side is equal to 640 an

DeepCam Shenzhen 1.4k Jan 07, 2023
WTTE-RNN a framework for churn and time to event prediction

WTTE-RNN Weibull Time To Event Recurrent Neural Network A less hacky machine-learning framework for churn- and time to event prediction. Forecasting p

Egil Martinsson 727 Dec 28, 2022
QMagFace: Simple and Accurate Quality-Aware Face Recognition

Quality-Aware Face Recognition 26.11.2021 start readme QMagFace: Simple and Accurate Quality-Aware Face Recognition Research Paper Implementation - To

Philipp Terhörst 59 Jan 04, 2023
R interface to fast.ai

R interface to fastai The fastai package provides R wrappers to fastai. The fastai library simplifies training fast and accurate neural nets using mod

113 Dec 20, 2022
Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

gym_multirotor Gym to train reinforcement learning agents on UAV platforms Quadrotor Tiltrotor Requirements This package has been tested on Ubuntu 18.

Aditya M. Deshpande 19 Dec 29, 2022
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark Yong

19 Dec 17, 2022
v objective diffusion inference code for PyTorch.

v-diffusion-pytorch v objective diffusion inference code for PyTorch, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The

Katherine Crowson 635 Dec 30, 2022
Adaout is a practical and flexible regularization method with high generalization and interpretability

Adaout Adaout is a practical and flexible regularization method with high generalization and interpretability. Requirements python 3.6 (Anaconda versi

lambett 1 Feb 09, 2022
Official Implementation of "DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization."

DialogLM Code for AAAI 2022 paper: DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization. Pre-trained Models We release two ve

Microsoft 92 Dec 19, 2022
python 93% acc. CNN Dogs Vs Cats ( Pytorch )

English | 简体中文(测试中...敬请期待) Cnn-Classification-Dog-Vs-Cat 猫狗辨别 (pytorch版本) CNN Resnet18 的猫狗分类器,基于ResNet及其变体网路系列,对于一般的图像识别任务表现优异,模型精准度高达93%(小型样本)。 项目制作于

apple ye 1 May 22, 2022
Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.

AI Choreographer: Music Conditioned 3D Dance Generation with AIST++ [ICCV-2021]. Overview This package contains the model implementation and training

Google Research 365 Dec 30, 2022
Constraint-based geometry sketcher for blender

Constraint-based sketcher addon for Blender that allows to create precise 2d shapes by defining a set of geometric constraints like tangent, distance,

1.7k Dec 31, 2022
😊 Python module for face feature changing

PyWarping Python module for face feature changing Installation pip install pywarping If you get an error: No such file or directory: 'cmake': 'cmake',

Dopevog 10 Sep 10, 2021
A simple software for capturing human body movements using the Kinect camera.

KinectMotionCapture A simple software for capturing human body movements using the Kinect camera. The software can seamlessly save joints and bones po

Aleksander Palkowski 5 Aug 13, 2022