velocyto.R
RNA velocity estimation in R
System requirements
velocyto.R can be installed on unix-flavored systems, and requires the following key elements:
- C++11
- Open MP support
- boost libaries
- igraph library
- hdf5c++ library (as required by the h5 R package to support loom files)
Installation
The easiest way to install velocyto.R is using devtools::install_github() from R:
library(devtools)
install_github("velocyto-team/velocyto.R")
You need to have boost (e.g. sudo apt-get install libboost-dev) and openmp libraries installed. You can see detailed installation commands in the dockers/debian9/Dockerfile.
Dockers
If you are having trouble installing the package on your system, you can build a docker instance that can be used on a wide range of systems and cloud environments. To install docker framework on your system see installation instruction. After installing the docker system, use the following commands to build a velocyto.R docker instance:
cd velocyto.R/dockers/debian9
docker build -t velocyto .
docker run --name velocyto -it velocytoTutorials
Chromaffin / SMART-seq2
The example shows how to annotate SMART-seq2 reads from bam file and estimate RNA velocity.
Dentate Gyrus / loom
The example shows how to load spliced/unspliced matrices from loom files prepared by velocyto.py CLI, use pagoda2 to cluster/embed cells, and then visualize RNA velocity on that embedding.
Mouse BM / dropEst
This example shows how to start analysis using dropEst count matrices, which can calculated from inDrop or 10x bam files using dropEst pipeline. It then uses pagoda2 to cluster/embed cells, and then visualize RNA velocity on that embedding.