tverbeiren/scope repository overview

⁠SCope v1.3.4: Visualization of large-scale and high dimensional single cell data

SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Currently the data format supported by SCope is .loom. This file format for very large omics datasets is maintained by the Linnarsson Lab through the loompy Python package (https://github.com/linnarsson-lab/loompy⁠).

⁠Version History

November 16, 2018

• Version 1.3.4
  • Fix some installation bugs.
  • Fix bug Compare tab disabled when a .loom does not contain any meta data information (e.g.: annotations).

August 22, 2018

• Version 1.3.2
  • Fix bug downloading sub loom when parent loom is a public one.
  • Fix bug downloading sub loom when parent loom has been created with SCopeLoomR version < 0.0.5.6558.
• Version 1.3.1
  • Fix bug when cluster markers not present in .loom.
• Version 1.3.0
  • Add feature to download subset of looms. Currently it is only possible to subset the active loom based on cluster information.

August 10, 2018

• Version 1.2.1
  • Fix bug when trajectory data not well defined in .loom.

July 6, 2018

• Version 1.2.0
  • Add feature to display trajectory data in the viewer. Currently the only way we provide to add trajectory data to .loom files is through SCopeLoomR⁠.

July 4, 2018

• Version 1.1.0
  • Add feature to display metrics in the viewer. Currently the only way we provide to add metrics to .loom files is through SCopeLoomR⁠.

⁠Demo

Visit http://scope.aertslab.org⁠ to test out SCope on several published datasets! Personal loom file files can be uploaded but will only be kept for 5 days.

⁠Loom File Generation

Currently there are two packages to generate extended loom files compatible with SCope.

• R: SCopeLoomR⁠ - Dedicated R package
• Python: pySCENIC⁠ - Single function for generation from SCENIC results

Eventually the functionality from pySCENIC will be expanded and put in its own python package.

⁠Run SCope

⁠Standalone apps

Standalone apps for macOS and Linux can be downloaded from the releases page.⁠.

A Windows app is under development, but currently has no ETA.

⁠Development

Requirements should be fulfilled (see Requirements section).

⁠Clone repository

# Define where you want to clone the SCope repository.
LOCAL_SCOPE_REPO="${HOME}/repos/SCope"

# Clone SCope git repository.
git clone https://github.com/aertslab/SCope "${LOCAL_SCOPE_REPO}"

⁠Install miniconda and create SCope environment.

We recommend using miniconda to install SCope and its dependencies in a clean environment.

Download miniconda3 from https://conda.io/miniconda.html⁠ or use the command line:

wget --content-disposition http://bit.ly/miniconda3

Install Minconda:

bash Miniconda3-latest-[...].sh

Create miniconda virtual environment with python (3.6 or higher) and nodejs (9 or higher) for SCope:

# Install python and nodejs with conda.
conda create -n scope 'python>=3.6' 'nodejs>=9'

⁠Install SCope

Activate miniconda virtual environment with python (3.6 or higher) and nodejs (9 or higher) and install SCope:

# Activate SCope environment.
conda activate scope

# Go to your local cloned SCope repository.
cd "${LOCAL_SCOPE_REPO}"

# Install SCope.
npm install

⁠Run

• One Command Run:

# Go to your local cloned SCope repository.
cd "${LOCAL_SCOPE_REPO}"

npm run scope

• Debug Run in 2 terminals:

# Go to your local cloned SCope repository.
cd "${LOCAL_SCOPE_REPO}"

# Start SCope Server (terminal 1).
scope-server

# Start SCope Client (terminal 2).
npm run dev

⁠1. Packaging SCope Data Server

Activate SCope environment (see Install miniconda and create SCope environment. section if you do not have one yet):

# Activate SCope environment.
conda activate scope

# Go to your local cloned SCope repository.
cd "${LOCAL_SCOPE_REPO}"

Install the SCope Server as Python package:

cd opt
python setup.py develop

Install PyInstaller:

cd scopeserver/dataserver
pip install pyinstaller

Package the SCope Data Server:

cd ./opt/scopeserver/dataserver
LD_LIBRARY_PATH=${CONDA_PATH}/lib pyinstaller \
	--onedir \
	--hidden-import=scipy._lib.messagestream \
	--hidden-import=pandas._libs.tslibs.timedeltas  \
	--hidden-import=cytoolz.utils \
	--hidden-import=cytoolz._signatures __init__.py \
	--hidden-import=pandas._libs.tslibs.np_datetime \
	--hidden-import=pandas._libs.tslibs.nattype \
	--hidden-import=pandas._libs.skiplist

${CONDA_PATH} is the path where Miniconda has been installed.

⁠2. Packaging SCope

First install electron-packager node module:

sudo npm install electron-packager -g

Finally, bundle the SCope app:

• Linux (x64)

npm run package-linux-x64
tar -zcvf scope-linux-x64.tar.gz scope-linux-x64

• macOS (x64)

npm run package-macOS-x64

Run the binary:

• Linux

./release/scope-linux-x64/scope

• macOS

Run the .app file generated

⁠3. Creating Single Executable File

⁠Debian package

For more details, follow https://www.christianengvall.se/electron-installer-debian-package/⁠

npm run create-debian-installer

⁠dmg for macOS

git clone https://github.com/andreyvit/yoursway-create-dmg.git
./yoursway-create-dmg/create-dmg \
	--volname "SCope Installer" \
	--volicon "images/SCope_Icon.icns" \
	--background "images/SCope_Background.png" \
	--window-pos 200 120 \
	--window-size 800 400 \
	--icon-size 100 \
	--icon release/scope-darwin-x64/scope.app 192 344 \
	--hide-extension scope.app \
	--app-drop-link 448 344 \
	${TRAVIS_BUILD_DIR}/release/scope-macOS-x64.dmg \
	release/scope-darwin-x64/scope.app/

All uploaded data from SCope will be put in the following folders by default:

• Linux ~/.local/share/scope/

• macOS ~/Library/Application\ Support/scope/

⁠Deploy a Cloud-based Instance

⁠Amazon Web Services

⁠Public AMI

Coming soon.

⁠Source

To create a SCope AWS instance from scratch please read the tutorial aws-deployment-source⁠.

⁠Architecture

SCope architecture can be visualized below: