Skip to content

Latest commit

 

History

History
164 lines (119 loc) · 8.32 KB

README.md

File metadata and controls

164 lines (119 loc) · 8.32 KB
Raw

Tutorial on "Gene regulatory network inference from single-cell transcriptomics data" (ISMB 2022)

Table of Contents

Schedule
Virtual Box Installation and Setup
  Windows and Linux
  OS X
Creating Virtual Machine
Running BEELINE
Running TENET
Resources
Tutorial Communication Channel

Schedule

11:00 am - 6:00 pm (Lunch Break 1:00 pm - 2:00 pm; Coffee Breaks at 3:15 - 3:30 and 4:45 - 5:00 pm)

10:45am-11:00pm: Arrival and coffee
11:00-11:30: Welcome, Introduction, plan for day and meet tutors/speakers
Intro and hands-on session slides

11:30-12:15: Talk 1: T. M. Murali How to Build Gene Regulatory Networks from Single-Cell RNA-seq data

12:15-13:00: Talk 2: Kedar Natarajan Inferring Gene Regulatory Networks and causal regulators using Transfer Entropy NETwork (TENET)

13:00-14:00: Lunch

14:00-15:15: GRN benchmarking using BEELINE (Hands-on)
Introduction to scRNA-seq, methods overview & principles
Introduction to generating synthetic datasets using BoolODE
Beeline benchmarking on scRNA-seq datasets

15:15-15:30: Break

15:30-16:45: BEELINE contd. (Hands-on)
Visualising and interpreting BEELINE results

16:45-17:00: Break

17:00-17:30: GRN inference using TENET (Hands-on)
Applying TENET on scRNA-seq datasets and visualisation

17:30-18:00: Discussions
Perspectives: Advantages, trade-offs and considerations for GRN inference methods
ISMB Tutorial feedback

Virtual Box Installation and Setup

Windows and Linux

Download and install Virtual Box.

OS X

Instructions (Mac with Intel architecture).

Tested on MBP 2019, 2.4Ghz Quad-Core intel i5, 8GB OS Monterey

  1. Download and install Virtual Box.
  2. When starting VirtualBox on intel mac, if you encounter kernel error, try below options a. Go to System Preferences > Security & Privacy and then allow VirtualBox to load b. Restart mac in recovery mode (Command (⌘) + R), goto Utilities tab and terminal. type "spctl kext-consent add VB5E2TV963". Restart again and repeat step (a) and restart virtualbox
  3. Follow the installation instructions below (see "Creating Virtual Machine")

Instructions (Mac with ARM/M1/Silicon architecture).

Tested on MBP 2021, Apple M1, 16GB OS Monterey Note: VirtualBox is not supported on MACs with ARM architecture and below are some suggestions that have worked for some users. We have not exhaustively tested the tutorial material on ARM architecture, and suggest users to use a non-ARM mac for current iteration of the workshop

  1. Download and [install] Parallels. Allow access to downloads folder.
  2. Free Download Ubuntu 20.04.2 ARM64 (2.37GB, free) within Parallels GUI (~3-5min; ~30MB/sec)
  3. Create a 15 day trial account, create password for the Ubuntu OS
  4. Follow the steps 1-3 (from linux instructions).
  5. If you encounter error during virtual box installation, try below steps
    a. sudo apt-get update
    b. sudo dpkg -i --force-architecture Downloads/virtualbox-6.1_6.1.34-150636.1Ubuntueoan_amd64.deb
    c. If you see a kernel error, try below
    d. echo "deb [arch=amd64] https://download.virtualbox.org/virtualbox/debian $(lsb_release -sc) contrib" | sudo tee /etc/apt/sources.list.d/virtualbox.list
    e. wget https://www.virtualbox.org/download/oracle_vbox_2016.asc
    f. sudo apt-key add oracle_vbox_2016.asc
    g. sudo apt update
    h. sudo apt install virtualbox-6.1
  6. Follow the installation instructions below (see "Creating Virtual Machine")

Creating A Virtual Machine

  1. Download the pre-configured Virtual Machine(VM) image.
  2. Create a VM by importing the downloaded VM image.

You would have now created a VM with the following properties -
- VM Name: ISMB2022-GRN-VM
- Operating System: Ubuntu 20.04.1 LTS (64bit)
- Memory(RAM): 2GB
- Root access:
- username: ismb2022-grn
- password: root
- VM contains:
- Docker v20.10.7
- Anaconda v4.12.0
- Python v3.9.12
- Java v11.0.15
- Cytoscape v3.9.1
- GraphSpace Python Client v1.0.0
- BEELINE installation and configurations (/home/ismb2022-grn/ISMB2022-GRN-Workshop/Beeline)
- TENET installation and configurations (/home/ismb2022-grn/ISMB2022-GRN-Workshop/TENET)

Note: To enable full screen mode for the virtual machine, you can use the short-cut: Host (Right Ctrl + F) or use the toolbar: View > Full-screen mode

Running BEELINE

All steps and commands to run BEELINE are in pre-configured Jupyter notebook ISMB 2022 GRN Tutorial on BEELINE.ipynb. Through this notebook we will perform the following steps -

  1. Activate BEELINE
  2. Run and evaluate GRN inference algorithms using BLRunner and BLEvaluator, respectively on following datasets
    1. Synthetic datasets
    2. Curated datasets
  3. Visualize the performance of the algorithms using BLPlotter

To start the Jupyter Notebook -

  • Double click the shell script ISMB2022-BEELINE-GRN-Notebook.sh on the Desktop

OR

  • Open a terminal and execute the command: . /home/ismb2022-grn/Desktop/ISMB2022-BEELINE-GRN-Notebook.sh

Please wait for 5-10 seconds for the Jupyter Notebook to load and show up in the browser.

Once the Jupyter Notebook has started, execute all the cells in the notebook sequentially.

Running TENET

All steps and commands to run TENET are in pre-configured Jupyter notebook ISMB 2022 GRN Tutorial on TENET.ipynb. Through this notebook we will perform the following steps -

  1. Activate TENET
  2. Perform GRN inference on -
    1. Synthetic dataset
    2. Experimental dataset

To start up the Jupyter Notebook -

  • Double click the shell script ISMB2022-TENET-GRN-Notebook.sh on the Desktop

OR

  • Open a terminal and execute the command: . /home/ismb2022-grn/Desktop/ISMB2022-TENET-GRN-Notebook.sh

Please wait for 5-10 seconds for the Jupyter Notebook to load and show up in the browser.

Once the Jupyter Notebook has started, execute all the cells in the notebook sequentially.

Resources

Introduction to single-cell transcriptomics data analysis

  1. Single-cell analysis course from Wellcome Sanger Institute
  2. Single-cell analysis course from Broad Institute

GRN inference methods, benchmarking, and reviews

  1. TENET
  2. BEELINE
  3. Reviews on GRN network inference
    1. Gene regulatory network inference in single-cell biology
    2. Mapping gene regulatory networks from single-cell omics data

Tutorial Communication Channel

Please use the ISMB-2022 Tutorial: GRN inference from scRNA-seq Slack channel for technical support during the tutorial, to initiate discussions, and communicate with participants and speakers.