https://github.com/laminetourelab/singlecell-rnaseq-tools
This repository contains single cell analysis pipelines.
https://github.com/laminetourelab/singlecell-rnaseq-tools
cellrank expimap rnavelocity scvelo single-cell single-cell-analysis single-cell-genomics single-cell-rna-seq velocity
Last synced: 5 months ago
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This repository contains single cell analysis pipelines.
- Host: GitHub
- URL: https://github.com/laminetourelab/singlecell-rnaseq-tools
- Owner: LamineTourelab
- Created: 2023-11-16T13:10:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-16T14:38:35.000Z (over 1 year ago)
- Last Synced: 2024-11-08T14:47:54.634Z (7 months ago)
- Topics: cellrank, expimap, rnavelocity, scvelo, single-cell, single-cell-analysis, single-cell-genomics, single-cell-rna-seq, velocity
- Language: Jupyter Notebook
- Homepage:
- Size: 1.91 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SingleCell-rnaseq Workflows
This repository contains single cell analysis pipelines.
## RNA Velocity
RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours.
### With scVelo
scVelo can be used to analysis RNA velocity, latent time, driver identification. See [scVelo](https://scvelo.readthedocs.io/en/stable/).
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](https://velocyto.org/velocyto.py/index.html).
+ 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
```
### Cellrank using rna velocity
Using the same conda environment ```RNAvelocity```
Install cellrank with conda:
```conda install -c conda-forge cellrank```
## Expimap
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
```