README.md

In velocyto-team/velocyto.R: RNA velocity estimation in R

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 velocyto

Tutorials

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.

velocyto-team/velocyto.R documentation built on Dec. 8, 2019, 3:07 a.m.