LIGER (liger) is a package for integrating and analyzing multiple single-cell datasets, developed and maintained by the Macosko lab. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors.

Check out our Cell paper for a more complete description of the methods and analyses. To access data used in our SN and BNST analyses, visit our study on the Single Cell Portal.

LIGER can be used to compare and contrast experimental datasets in a variety of contexts, for instance:

• Across experimental batches
• Across individuals
• Across sex
• Across tissues
• Across species (e.g., mouse and human)
• Across modalities (e.g., scRNAseq and spatial transcriptomics data, scMethylation, or scATAC-seq)

Once multiple datasets are integrated, the package provides functionality for further data exploration, analysis, and visualization. Users can:

• Identify clusters
• Find significant shared (and dataset-specific) gene markers
• Compare clusters with previously identified cell types
• Visualize clusters and gene expression using t-SNE and UMAP

We have also designed LIGER to interface with existing single-cell analysis packages, including Seurat.

Consider filling out our feedback form to help us improve the functionality and accesibility of LIGER.

For usage examples and guided walkthroughs, check the vignettes directory of the repo.

• Integrating Multiple Single-Cell RNA-seq Datasets

• Jointly Defining Cell Types from scRNA-seq and scATAC-seq

• Jointly Defining Cell Types from Single-Cell RNA-seq and DNA Methylation

• Running Liger directly on Seurat objects using Seurat wrappers

• Iterative Refinement of Cellular Identity Using Online iNMF

The liger package requires only a standard computer with enough RAM to support the in-memory operations. For minimal performance, please make sure that the computer has at least about 2 GB of RAM. For optimal performance, we recommend a computer with the following specs:

• RAM: 16+ GB
• CPU: 4+ cores, 2.3 GHz/core

The package development version is tested on Linux operating systems and Mac OSX.

• Linux: CentOS 7, Manjaro 5.3.18
• Mac OSX: Mojave (10.14.1), Catalina (10.15.2)

The liger package should be compatible with Windows, Mac, and Linux operating systems.

Before setting up the liger package, users should have R version 3.4.0 or higher, and several packages set up from CRAN and other repositories. The user can check the dependencies in DESCRIPTION.

liger is written in R and has a few other system requirements (Java) and recommended packages (umap in Python). To install the most recent development version, follow these instructions:

1. Install R (>= 3.4)
2. Install Rstudio (recommended)
3. Make sure you have Java installed in your machine. Check by typing java -version into Terminal or Command Prompt.
4. Use the following R commands.

install.packages('devtools')
library(devtools)
install_github('MacoskoLab/liger')

Installing RcppArmadillo on R>=3.4 requires Clang >= 4 and gfortran-6.1. Follow the instructions below if you have R version 3.4.0-3.4.4. These instructions (using clang4) may also be sufficient for R>=3.5 but for newer versions of R, it's recommended to follow the instructions in this post.

1. Install gfortran as suggested here
2. Download clang4 from this page
3. Uncompress the resulting zip file and type into Terminal (sudo if needed):

mv /path/to/clang4/ /usr/local/ 

4. Create .R/Makevars file containing following:

# The following statements are required to use the clang4 binary
CC=/usr/local/clang4/bin/clang
CXX=/usr/local/clang4/bin/clang++
CXX11=/usr/local/clang4/bin/clang++
CXX14=/usr/local/clang4/bin/clang++
CXX17=/usr/local/clang4/bin/clang++
CXX1X=/usr/local/clang4/bin/clang++
LDFLAGS=-L/usr/local/clang4/lib

For example, use the following Terminal commands:

cd ~
mkdir .R
cd .R 
nano Makevars

Paste in the required text above and save with Ctrl-X.

If installing natively is difficult, you can run liger through our Docker image (available publically), which also comes with Rstudio and Seurat (v2) installed.

1. Install Docker.
2. Run the following in terminal:

docker run -d -p 8787:8787 docker.io/vkozareva/sc-liger:latest

3. Type http://localhost:8787 in any browser and enter "rstudio" as the username and password when prompted. liger and all of its dependencies are already installed in this environment.

If you wish to access local files in this container (mounting to /data) modify the command as follows:

docker run -d -v /path/to/local/directory:/data -p 8787:8787 docker.io/vkozareva/sc-liger:latest

Note that you will have to stop the container if you wish to allocate port 8787 to another application later on. Further Docker documentation can be found here.

Using FIt-SNE is recommended for computational efficiency when using runTSNE on very large datasets. Installing and compiling the necessary software requires the use of git, FIt-SNE, and FFTW. For a basic overview of installation, visit this page.

Basic installation for most Unix machines can be achieved with the following commands after downloading the latest version of FFTW from here. In the fftw directory, run:

./configure
make
make install

(Additional instructions if necessary). Then in desired directory:

git clone https://github.com/KlugerLab/FIt-SNE.git
cd FIt-SNE
g++ -std=c++11 -O3  src/sptree.cpp src/tsne.cpp src/nbodyfft.cpp  -o bin/fast_tsne -pthread -lfftw3 -lm
pwd

Use the output of pwd as the fitsne.path parameter in runTSNE.

Note that the above instructions require root access. To install into a specified folder (such as your home directory) on a server, use the --prefix option:

./configure --prefix=<install_dir>
make
make install
git clone https://github.com/KlugerLab/FIt-SNE.git
cd FIt-SNE
g++ -std=c++11 -O3  src/sptree.cpp src/tsne.cpp src/nbodyfft.cpp  -I<install_dir>/include/ -L<install_dir>/lib/ -o bin/fast_tsne -pthread -lfftw3 -lm
pwd

The installation process of liger should take less than 30 minutes.

The expected run time is 1 - 4 hours depending on dataset size and downstream analysis of the user’s choice.

The liger package provides a small simulated dataset for basic demos of the functions, you can find it in folder liger/tests/testdata/small_pbmc_data.RDS.

We also provide a set of scRNA-seq and scATAC-seq datasets for real-world style demos. These datasets are as follows:

• scATAC and scRNA data provided by 10X Genomics, access the pre-processed data from here. The data sources are:

  • pbmc.atac.expression.mat.RDS: raw data can be accessed here, created by Cell Ranger ATAC 1.1.0;
  • pbmc.rna.expression.mat.RDS: raw data can be accessed here, created by Cell Ranger 3.0.0.

• scATAC and scRNA data provided by GreenleafLab; you can access the pre-processed data from here:

  • GSM4138872_scRNA_BMMC_D1T1.RDS;
  • GSM4138873_scRNA_BMMC_D1T2.RDS;
  • GSM4138888_scATAC_BMMC_D5T1_peak_counts.RDS;
  • GSM4138888_scATAC_BMMC_D5T1.RDS.

• scRNA data composed of two datasets of interneurons and oligodendrocytes from the mouse frontal cortex, two distinct cell types that should not align if integrated. Provided by Saunders, A. et.al., 2018; you can access the pre-processed data from here:

  • interneurons_and_oligo.RDS;

• scRNA data from control and interferon-stimulated PBMCs. Raw data provided by Kang, et.al., 2017; The datasets were downsampled by applying the sample function without replacement yield 3000 cells for each matrix. You can download downsampled data from here:

  • PBMC_control.RDS;
  • PBMC_interferon-stimulated.RDS.

Corresponding tutorials can be found in section Usage above.

This project is covered under the GNU General Public License 3.0.