This repository contains single cell analysis pipelines.
RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours.
scVelo can be used to analysis RNA velocity, latent time, driver identification. See scVelo.
First of all, the input data for scVelo are two count matrices of pre-mature (unspliced) and mature (spliced) abundances, which can be obtained from standard sequencing protocols, using the velocyto or kallisto counting pipeline. Here is velocyto example:
velocyto run10x -m repeat_msk.gtf mypath/sample01 somepath/refdata-cellranger-mm10-1.2.0/genes/genes.gtf
Where genes.gtf is the genome annotation file provided with the cellranger pipeline. repeat_msk.gtf is the repeat masker file. See Velocyto.org.
- Create a conda environment with the latest python version. Here
3.11.5.
conda create -n RNAvelocity
- Install the following packages versions
pip install numpy==1.21.1 pandas==1.1.5 matplotlib==3.7.3 scanpy==1.9.6 igraph==0.9.8 scvelo==0.2.5 loompy==3.0.6 anndata==0.8.0
The following packages may be needed
pip install tqdm
pip install ipywidgets
Using the same conda environment RNAvelocity
Install cellrank with conda:
conda install -c conda-forge cellrank
ExpiMap learns to map cells into biologically understandable components representing known ‘gene programs’. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs.
git clone https://github.com/theislab/scarches
cd scarches
conda env create -f envs/scarches_linux.yaml
conda activate scarches