update and add more Rmd doc

README.md

@@ -4,6 +4,8 @@
 
 # LIGER (Linked Inference of Genomic Experimental Relationships)
 
+>Now we have a comprehensive documentation site for the latest development version of [rliger 2.0](https://mvfki.github.io/liger/index.html)!
+
 LIGER (installed as `rliger` ) 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://doi.org/10.1016/j.cell.2019.05.006) 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
@@ -30,9 +32,11 @@ We have also designed LIGER to interface with existing single-cell analysis pack
 [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.
 
 * [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)
@@ -49,126 +53,9 @@ For usage examples and guided walkthroughs, check the `vignettes` directory of t
 
 * [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 `rliger` 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 `rliger` package should be compatible with Windows, Mac, and Linux operating systems.
-
-Before setting up the `rliger` 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 also available on the Comprehensive R Archive Network (CRAN). Note that the package name 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/)  (>= 3.6)
-2. Install [Rstudio](https://posit.co/download/rstudio-desktop/) (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')
-```
-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](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](https://mac.R-project.org/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
-Note that the runUMAP function (which calls the `uwot` 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](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 `rliger` 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 `rliger` package provides a small sample 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:
@@ -190,8 +77,3 @@ We also provide a set of scRNA-seq and scATAC-seq datasets for real-world style
 * scATAC and scRNA data provided by 10X Genomics. The data sources are:
     + `pbmc.atac.expression.mat.RDS`: raw data can be accessed [here](https://support.10xgenomics.com/single-cell-atac/datasets/1.1.0/atac_v1_pbmc_10k), created by Cell Ranger ATAC 1.1.0;
     + `pbmc.rna.expression.mat.RDS`: raw data can be accessed [here](https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_v3), created by Cell Ranger 3.0.0.
-
-Corresponding tutorials can be found in section **Usage** above.
-
-## License
-This project is covered under the **GNU General Public License 3.0**.

_pkgdown.yml

@@ -95,7 +95,7 @@ navbar:
   title: "rliger"
   left:
     - text: "Installation"
-      href: #
+      href: articles/installation.html
     - text: "Tutorials"
       menu:
         - text: "Integrating multiple scRNAseq data"