Need help with liger?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

215 Stars 60 Forks GNU General Public License v3.0 616 Commits 24 Opened issues


R package for integrating and analyzing multiple single-cell datasets

Services available


Need anything else?

Contributors list

Build Status

LIGER (Linked Inference of Genomic Experimental Relationships)

LIGER (installed as

) is a package for integrating and analyzing multiple single-cell datasets, developed by the Macosko lab and maintained/extended by the Welch 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 accessibility of LIGER.


For usage examples and guided walkthroughs, check the

directory of the repo.

System Requirements

Hardware requirements


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

Software requirements

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)


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

Before setting up the

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


LIGER is written in R and is also available on the Comprehensive R Archive Network (CRAN). Note that the package name is

to avoid a naming conflict with an unrelated package. To install the version on CRAN, follow these instructions:
  1. Install R (>= 3.4)
  2. Install Rstudio (recommended)
  3. Type the following R command:
    To install the latest development version directly from GitHub, type the following commands instead of step 3:
    Note that the GitHub version requires installing from source, which may involve additional installation steps on MacOS (see below).

Additional Steps for Installing LIGER from Source (recommended before step 3)

Installation from CRAN is easy because pre-compiled binaries are available for Windows and MacOS. However, a few additional steps are required to install from source on MacOS/Windows (e.g. Install RcppArmadillo). (MacOS) Installing RcppArmadillo on R>=3.4 requires Clang >= 4 and gfortran-6.1. For newer versions of R (R>=3.5), it's recommended to follow the instructions in this post. Follow the instructions below if you have R version 3.4.0-3.4.4.

  1. Install gfortran as suggested here
  2. Download clang4 from this page
  3. Uncompress the resulting zip file and type into Terminal (
    if needed):
    mv /path/to/clang4/ /usr/local/ 
  4. Create
    file containing following:
    # The following statements are required to use the clang4 binary
    For example, use the following Terminal commands:
    cd ~
    mkdir .R
    cd .R 
    nano Makevars
    Paste in the required text above and save with

Additional Installation Steps for Online Learning using LIGER

The HDF5 library is required for implementing online learning in LIGER on data files in HDF5 format. It can be installed via one of the following commands:

| System | Command |:------------------------------------------|:---------------------------------| |OS X (using Homebrew or Conda) |

brew install hdf5
conda install -c anaconda hdf5
|Debian-based systems (including Ubuntu)|
sudo apt-get install libhdf5-dev
|Systems supporting yum and RPMs |
sudo yum install hdf5-devel

For Windows, the latest HDF5 1.12.0 is available at

Detailed Instructions for FIt-SNE Installation

Note that the runUMAP function (which calls the

package) also scales to large datasets and does not require additional installation steps. However, using FIt-SNE is recommended for computational efficiency if you want to perform t-SNE 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:

make install
(Additional instructions if necessary). Then in desired directory:
git clone
cd FIt-SNE
g++ -std=c++11 -O3  src/sptree.cpp src/tsne.cpp src/nbodyfft.cpp  -o bin/fast_tsne -pthread -lfftw3 -lm
Use the output of
as the
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

./configure --prefix=
make install
git clone
cd FIt-SNE
g++ -std=c++11 -O3  src/sptree.cpp src/tsne.cpp src/nbodyfft.cpp  -I/include/ -L/lib/ -o bin/fast_tsne -pthread -lfftw3 -lm

Install Time and Expected Run Time

The installation process of

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.

Sample Datasets


package provides a small sample dataset for basic demos of the functions. You can find it in folder

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., 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,, 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.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.