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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
# dynverse
**dynverse** is created to support the development, execution, and benchmarking of trajectory inference methods. Check out our preprint on bioRxiv:
Wouter Saelens\*, Robrecht Cannoodt\*, Helena Todorov, Yvan Saeys. “A comparison of single-cell trajectory inference methods: towards more accurate and robust tools”. bioRxiv (Mar. 2018). DOI: [10.1101/276907](https://doi.org/10.1101/276907).
\*: Equal contribution
## Subpackages
dynverse consists of several subpackages:
```{r, echo = FALSE, warning=FALSE, error=FALSE, message = FALSE}
library(tidyverse)
df <- tribble(
~Repo, ~Description,
"dynalysis", "Scripts to reproduce manuscript",
"dynwrap", "Common trajectory wrapping functionality",
"dynmethods", "Wrappers for trajectory inference methods",
"dyneval", "Metrics and evaluation pipeline",
"dyngen", "Generator of synthetic datasets",
"dynnormaliser", "Common normalisation functionality",
"dyntoy", "Quick generator of small toy datasets",
"dynplot", "Common visualisation functionality",
"dynutils", "Various common functions"
) %>%
mutate(
Package = glue::glue("[{Repo}](https://github.com/dynverse/{Repo})"),
Status = ifelse(!Repo %in% c("dynalysis"), glue::glue("[](https://travis-ci.org/dynverse/{Repo})"), "Coming soon")
) %>%
select(Package, Status, Description)
knitr::kable(df)
```
## Datasets
We include the following datasets. When using any of these datasets, please also cite our preprint: [](https://doi.org/10.1101/276907)
* [](https://doi.org/10.5281/zenodo.1211533) Single-cell -omics datasets, both real and synthetic, used to evaluated the trajectory inference methods
* **Coming soon** Main results of the evaluation, used to rank the methods and construct practical guidelines
## Installation
Supported platforms are Linux and Mac OS X.
Windows users *could* use [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install-win10) in order to run these packages on a Windows machine.
### Debian / Ubuntu / Linux Mint
First you need to install a few packages:
```{r, engine = 'bash', eval = FALSE}
sudo apt-get install libudunits2-dev libgsl-dev libsdl1.2-dev libreadline-dev imagemagick libfftw3-dev libudunits2-dev librsvg2-dev -y
```
The installation of dynmethods is sped up by preinstalling several Python libraries:
```{r, engine = 'bash', eval = FALSE}
sudo apt-get install python2.7-dev python3-dev python3-pip
pip3 install --user virtualenv numpy matplotlib pandas six jinja2 python-dateutil pytz pyparsing cycler tqdm python-igraph rpy2 Cython scipy statsmodels sklearn seaborn h5py anndata
```
Finally, you can install the dynverse packages with devtools:
```{r, eval = FALSE}
install.packages("devtools")
library(devtools)
install_github("dynverse/dynwrap", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynmethods", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dyneval", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynnormaliser", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dyntoy", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynplot", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynutils", dependencies = TRUE, build_vignettes = TRUE)
```
### Fedora / CentOS
First you need to install a few packages:
```{r, engine = 'bash', eval = FALSE}
sudo dnf install openssl-devel libcurl-devel udunits2-devel libxml2-devel gsl-devel SDL2-devel readline-devel ImageMagick-c++-devel SDL-devel openblas-devel lapack-devel librsvg2-devel
```
The installation of dynmethods is sped up by preinstalling several Python libraries:
```{r, engine = 'bash', eval = FALSE}
sudo dnf install python2-devel python3-devel python3-pip python3-matplotlib-tk
pip3 install --user virtualenv numpy matplotlib pandas six jinja2 python-dateutil pytz pyparsing cycler tqdm python-igraph rpy2 Cython scipy statsmodels sklearn seaborn h5py anndata
Rscript -e 'install.packages("udunits2", configure.args = c(udunits2 = '--with-udunits2-include=/usr/include/udunits2'))'
```
Finally, you can install the dynverse packages as follows:
```{r, eval = FALSE}
install.packages("devtools")
library(devtools)
install_github("dynverse/dynwrap", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynmethods", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dyneval", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynnormaliser", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dyntoy", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynplot", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynutils", dependencies = TRUE, build_vignettes = TRUE)
```
### Mac OS X
First you will need to install the runtime binary of [SDL1.2](https://www.libsdl.org/download-1.2.php) and the development binary of [SDL2](https://www.libsdl.org/download-2.0.php) manually. Also install `librsvg` with brew.
The installation of dynmethods is sped up by preinstalling several Python libraries:
```{r, engine = 'bash', eval = FALSE}
pip3 install --user virtualenv numpy matplotlib pandas six jinja2 python-dateutil pytz pyparsing cycler tqdm python-igraph rpy2 Cython scipy statsmodels sklearn seaborn h5py anndata
```
Finally, you can install the dynverse packages as follows:
```{r, eval = FALSE}
install.packages("devtools")
library(devtools)
install_github("dynverse/dynwrap", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynmethods", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dyneval", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynnormaliser", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dyntoy", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynplot", dependencies = TRUE, build_vignettes = TRUE)
install_github("dynverse/dynutils", dependencies = TRUE, build_vignettes = TRUE)
```