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)
Controlling Hill Climb Racing with Hand Tacking

Controlling Hill Climb Racing with Hand Tacking Opened Palm for Gas Closed Palm for Brake

Rohit Ingole 3 Jan 18, 2022
Deep ViT Features as Dense Visual Descriptors

dino-vit-features [paper] [project page] Official implementation of the paper "Deep ViT Features as Dense Visual Descriptors". We demonstrate the effe

Shir Amir 113 Dec 24, 2022
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传,直接clone后,运行yolo_win.py即可开启界面。 2021/9/29:加入置信度选择 界面是在ultralytics的yolov5基础上建立的,界面使用pyqt5实现,内容较简单,娱乐而已。 功能: 模型选择 本地文件选择(视频图片均可) 开关摄像头

487 Dec 27, 2022
scikit-learn inspired API for CRFsuite

sklearn-crfsuite sklearn-crfsuite is a thin CRFsuite (python-crfsuite) wrapper which provides interface simlar to scikit-learn. sklearn_crfsuite.CRF i

417 Dec 20, 2022
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]

A 2D Visual Localization Framework based on Essential Matrices This repository provides implementation of our paper accepted at ICRA: To Learn or Not

Qunjie Zhou 27 Nov 07, 2022
pixelNeRF: Neural Radiance Fields from One or Few Images

pixelNeRF: Neural Radiance Fields from One or Few Images Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa UC Berkeley arXiv: http://arxiv.org/abs/2

Alex Yu 1k Jan 04, 2023
Romanian Automatic Speech Recognition from the ROBIN project

RobinASR This repository contains Robin's Automatic Speech Recognition (RobinASR) for the Romanian language based on the DeepSpeech2 architecture, tog

RACAI 10 Jan 01, 2023
🐥A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

PyTorch implementation of OpenAI's Finetuned Transformer Language Model This is a PyTorch implementation of the TensorFlow code provided with OpenAI's

Hugging Face 1.4k Jan 05, 2023
Rotation Robust Descriptors

RoRD Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching Project Page | Paper link Evaluation and Datasets MMA : Training on

Udit Singh Parihar 25 Nov 15, 2022
Square Root Bundle Adjustment for Large-Scale Reconstruction

RootBA: Square Root Bundle Adjustment Project Page | Paper | Poster | Video | Code Table of Contents Citation Dependencies Installing dependencies on

Nikolaus Demmel 205 Dec 20, 2022
thundernet ncnn

MMDetection_Lite 基于mmdetection 实现一些轻量级检测模型,安装方式和mmdeteciton相同 voc0712 voc 0712训练 voc2007测试 coco预训练 thundernet_voc_shufflenetv2_1.5 input shape mAP 320

DayBreak 39 Dec 05, 2022
PyTorch Implementation of Vector Quantized Variational AutoEncoders.

Pytorch implementation of VQVAE. This paper combines 2 tricks: Vector Quantization (check out this amazing blog for better understanding.) Straight-Th

Vrushank Changawala 2 Oct 06, 2021
Temporal Knowledge Graph Reasoning Triggered by Memories

MTDM Temporal Knowledge Graph Reasoning Triggered by Memories To alleviate the time dependence, we propose a memory-triggered decision-making (MTDM) n

4 Sep 25, 2022
This is my codes that can visualize the psnr image in testing videos.

CVPR2018-Baseline-PSNRplot This is my codes that can visualize the psnr image in testing videos. Future Frame Prediction for Anomaly Detection – A New

Wenhao Yang 12 May 29, 2021
Pytorch Implementation of Value Retrieval with Arbitrary Queries for Form-like Documents.

Value Retrieval with Arbitrary Queries for Form-like Documents Introduction Pytorch Implementation of Value Retrieval with Arbitrary Queries for Form-

Salesforce 13 Sep 15, 2022
Code for "Discovering Non-monotonic Autoregressive Orderings with Variational Inference" (paper and code updated from ICLR 2021)

Discovering Non-monotonic Autoregressive Orderings with Variational Inference Description This package contains the source code implementation of the

Xuanlin (Simon) Li 10 Dec 29, 2022
Official repo for SemanticGAN https://nv-tlabs.github.io/semanticGAN/

SemanticGAN This is the official code for: Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizat

151 Dec 28, 2022
Train Dense Passage Retriever (DPR) with a single GPU

Gradient Cached Dense Passage Retrieval Gradient Cached Dense Passage Retrieval (GC-DPR) - is an extension of the original DPR library. We introduce G

Luyu Gao 92 Jan 02, 2023
A machine learning malware analysis framework for Android apps.

🕵️ A machine learning malware analysis framework for Android apps. ☢️ DroidDetective is a Python tool for analysing Android applications (APKs) for p

James Stevenson 77 Dec 27, 2022