liger：R软件包，用于集成和分析多个单细胞数据集

<img src="docs/img/liger_cropped.png" width="150"> [](https://travis-ci.com/welch-lab/liger.svg?branch=master) # LIGER (Linked Inference of Genomic Experimental Relationships) LIGER (`liger`) 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](https://www.cell.com/cell/fulltext/S0092-8674%2819%2930504-5) for a more complete description of the methods and analyses. To access data used in our SN and BNST analyses, visit our [study](https://portals.broadinstitute.org/single_cell/study/SCP466) 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](https://satijalab.org/seurat/). ## Feedback Consider filling out our [feedback form](https://forms.gle/bhvp3K6tiHwf976r8) to help us improve the functionality and accessibility of LIGER. ## Usage For usage examples and guided walkthroughs, check the `vignettes` directory of the repo. * NEW: [Iterative Single-Cell Multi-Omic Integration Using Online iNMF](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/online_iNMF_tutorial.html) * [Integrating Multiple Single-Cell RNA-seq Datasets](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/Integrating_multi_scRNA_data.html) * [Jointly Defining Cell Types from scRNA-seq and scATAC-seq](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/Integrating_scRNA_and_scATAC_data.html) * [Jointly Defining Cell Types from Single-Cell RNA-seq and DNA Methylation](https://welch-lab.github.io/liger/rna-methylation.html) * [Running Liger directly on Seurat objects using Seurat wrappers](https://htmlpreview.github.io/?https://github.com/satijalab/seurat.wrappers/blob/master/docs/liger.html) ## System Requirements ### Hardware requirements 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 ### 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) 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`. ## Installation `liger` is written in R and is available on the Comprehensive R Archive Network (CRAN). Note that the package name on CRAN is `rliger` to avoid a naming conflict with an unrelated package. To install the version on CRAN, follow these instructions: 1. Install [R](https://www.r-project.org/) (>= 3.4) 2. Install [Rstudio](https://www.rstudio.com/products/rstudio/download/) (recommended) 3. Type the following R command: ``` install.packages('rliger') ``` To install the latest development version directly from GitHub, type the following commands instead of step 3: ``` install.packages('devtools') library(devtools) install_github('welch-lab/liger') ``` ### Additional Installation Steps for MacOS (recommended before step 3) 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](https://thecoatlessprofessor.com/programming/r-compiler-tools-for-rcpp-on-macos/). Follow the instructions below if you have R version 3.4.0-3.4.4. 1. Install gfortran as suggested [here](https://gcc.gnu.org/wiki/GFortranBinaries) 2. Download clang4 from this [page](http://r.research.att.com/libs/clang-4.0.0-darwin15.6-Release.tar.gz) 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`. ### 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` or `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 https://www.hdfgroup.org/downloads/hdf5/. ### Detailed Instructions for FIt-SNE Installation for use in runTSNE (recommended for large datasets) 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](https://github.com/KlugerLab/FIt-SNE). Basic installation for most Unix machines can be achieved with the following commands after downloading the latest version of FFTW from [here](http://www.fftw.org/). In the fftw directory, run: ``` ./configure make make install ``` (Additional [instructions](http://www.fftw.org/fftw3_doc/Installation-and-Customization.html) 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 ``` ### Install Time and Expected Run Time 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. ## Sample Datasets The `liger` package provides a small sample dataset for basic demos of the fun