Merge pull request #288 from mvfki/newObj

GitHub Actions Workflows

.github/workflows/r.yml

@@ -19,26 +19,53 @@ permissions:
 
 jobs:
   build:
-    runs-on: macos-latest
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
     strategy:
+      fail-fast: false
       matrix:
-        r-version: ['4.1.1']
+        config:
+          - {os: macOS-latest,   r: 'release'}
+          - {os: windows-latest, r: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
     steps:
       - uses: actions/checkout@v3
-      - name: Set up R ${{ matrix.r-version }}
-        uses: r-lib/actions/setup-r@f57f1301a053485946083d7a45022b278929a78a
+
+      - name: Set up R ${{ matrix.r }}
+        uses: r-lib/actions/setup-r@v2
         with:
-          r-version: ${{ matrix.r-version }}
+          r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
+
+      - name: Install library on Linux
+        if: runner.os == 'Linux'
+        run: |
+          sudo apt-get install libhdf5-dev
+          sudo apt-get install libglpk-dev
+
       - name: Install dependencies
         run: |
           install.packages(c("remotes", "rcmdcheck"))
           remotes::install_deps(dependencies = TRUE)
         shell: Rscript {0}
+
       - name: Check
         run: rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "warning")
         shell: Rscript {0}
+
+      - name: Run codecov
+        run: |
+          install.packages("covr")
+          covr::codecov()
+        shell: Rscript {0}
+
       - name: Upload coverage reports to Codecov
         uses: codecov/codecov-action@v3
 

README.md

@@ -1,13 +1,13 @@
-<img src="img/liger_cropped.png" width="150">
+<img src="https://github.com/mvfki/liger/raw/newObj/inst/extdata/logo.png" width="120">
 
-[![Build Status](https://api.travis-ci.com/welch-lab/liger.svg?branch=master)](https://api.travis-ci.com/welch-lab/liger.svg?branch=master)
+[![R](https://github.com/mvfki/liger/actions/workflows/r.yml/badge.svg?branch=newObj)](https://github.com/mvfki/liger/actions/workflows/r.yml)[![codecov](https://codecov.io/gh/mvfki/liger/branch/newObj/graph/badge.svg?token=77TTU4GY8A)](https://codecov.io/gh/mvfki/liger)
 
 # LIGER (Linked Inference of Genomic Experimental Relationships)
 
-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. 
+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://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. 
+Single Cell Portal.
 
 LIGER can be used to compare and contrast experimental datasets in a variety of contexts, for instance:
 
@@ -18,28 +18,28 @@ LIGER can be used to compare and contrast experimental datasets in a variety of
 * 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, 
+Once multiple datasets are integrated, the package provides functionality for further data exploration,
 analysis, and visualization. Users can:
 
-* Identify clusters 
+* 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 
+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. 
+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)
 * [Integrating unshared features with UINMF](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/UINMF_vignette.html)
 * [scATAC and scRNA Integration using unshared features (UINMF)](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/SNAREseq_walkthrough.html)
 * [Cross-species Analysis with UINMF](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/cross_species_vig.html)
-* [Performing Parameter Selection](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/Parameter_selection.html) 
+* [Performing Parameter Selection](http://htmlpreview.github.io/?https://github.com/welch-lab/liger/blob/master/vignettes/Parameter_selection.html)
 * [Integrating spatial transcriptomic and transcriptomic datasets using UINMF](https://www.dropbox.com/s/ho62az1kshj59d2/STARmap_dropviz_vig.html?dl=0) (Click to Download)
 * [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)
 
@@ -92,9 +92,9 @@ Installation from CRAN is easy because pre-compiled binaries are available for W
 
 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): 
+3. Uncompress the resulting zip file and type into Terminal (`sudo` if needed):
 ```
-mv /path/to/clang4/ /usr/local/ 
+mv /path/to/clang4/ /usr/local/
 ```
 4. Create `.R/Makevars` file containing following:
 ```
@@ -111,9 +111,9 @@ For example, use the following Terminal commands:
 ```
 cd ~
 mkdir .R
-cd .R 
+cd .R
 nano Makevars
-``` 
+```
 Paste in the required text above and save with `Ctrl-X`.
 
 ### Additional Installation Steps for Online Learning using LIGER
@@ -122,25 +122,25 @@ The HDF5 library is required for implementing online learning in LIGER on data f
 | 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` 
+|**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 
+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 
+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 
+(Additional [instructions](http://www.fftw.org/fftw3_doc/Installation-and-Customization.html) if
 necessary).
 Then in desired directory:
 ```
@@ -149,7 +149,7 @@ 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. 
+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:
 ```
@@ -172,7 +172,7 @@ The expected run time is 1 - 4 hours depending on dataset size and downstream an
 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:
-     
+
 * scRNA data from control and interferon-stimulated PBMCs. Raw data provided by [Kang, et.al., 2017](https://www.nature.com/articles/nbt.4042); The datasets were downsampled by applying the sample function without replacement yield 3000 cells for each matrix. You can download downsampled data from [here](https://www.dropbox.com/sh/u94ib3dkf9pb6nd/AABemvnxDgKDGRs8Ek5QGlXWa?dl=0):
      + `PBMC_control.RDS`;
      + `PBMC_interferon-stimulated.RDS`.