The goal of the BICCN Github repository is to facilitate reproducible dataset integrations for many analyses, standardizing the integration workflow across anatomical regions and major cell classes.
The datasets have been made publicly available at NeMO Analytics. The algorithm used for data integration is online iNMF, detailed in the publication Iterative single-cell multi-omic integration using online learning. We perform all analysis with rliger1.0.0.
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
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.
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. The installation time for the LIGER toolbox is ~15–30 min. To install the version on CRAN, follow these instructions:
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).
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.
- Install gfortran as suggested here
- Download clang4 from this page
- Uncompress the resulting zip file and type into Terminal (
sudoif needed):
mv /path/to/clang4/ /usr/local/
- Create
.R/Makevarsfile 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.
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/.
A vignette of online iNMF can found at Iterative Single-Cell Multi-Omic Integration Using Online iNMF.
To reproduce our results, please use the parameters provided in the supplementary materials of our submitted manuscript. The result of the BICCN pipeline is the datasets integrated and clustered. Run time is a direct product of the number of cells integrated.
For reproducibility, we provide a series of vignettes that detail the creation of the vascular, c-Fos, and neuronal projection analyses and figures as described in our publication. For details, see our website.
magrittr 2.0.3 varhandle 2.0.5 sjmisc 2.8.9 stringr 1.5.0 edgeR 3.38.4 openxlsx 4.2.5 devtools 2.4.4 scrattch.hicat 1.0.0 RANN 2.6.1 ggplot2 3.4.2 dplyr 1.1.2 tidyr 1.3.0 reshape 0.8.9 rgl 1.1.3 gridExtra 2.3 data.table 1.14.8