scVelo is a method to describe the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). It avoids errors in the velocity estimates by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations.

References:

• Volker Bergen, Marius Lange, Stefan Peidli, F. Alexander Wolf and Fabian J. Theis
  Generalizing RNA velocity to transient cell states through dynamical modeling
  Nature Biotechnology 38, 1408-1414 (2020).

• scVelo GitHub page
• scVelo Documentation

• Module Name: scVelo (see the modules page for more information)
• Unusual environment variables set
  • SCVELO_HOME  installation directory
  • SCVELO_BIN       executable directory
  • SCVELO_SRC       source code directory
  • SCVELO_DATA  sample data directory

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive --mem=4g
[user@cn0911 ~]$module load scvelo  
[+] Loading scvelo  0.2.4  on cn0911
[+] Loading singularity  3.8.5  on cn0911
[user@cn0911 ~]$python $SCVELO_SRC/tests/test_basic.py
[user@cn0911 ~]$python 
Python 3.8.12 (default, Oct 12 2021, 13:49:34)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np  
>>> import matplotlib.pyplot as pl  
>>> import scvelo as scv  

>>> scv.settings.set_figure_params('scvelo', dpi_save=200, dpi=80, transparent=True)  
>>> scv.settings.plot_prefix = 'scvelo_fig2_'  
>>> scv.settings.verbosity = 2  

>>> adata = scv.datasets.dentategyrus()  
100%|██████████████████████████████████████████████████████████████████████████████████████| 23.7M/23.7M [00:00<00:00, 71.1MB/s]

>>> scv.pp.filter_and_normalize(adata, min_shared_cells=20, n_top_genes=2000)  
Filtered out 11835 genes that are detected in less than 20 cells (shared).
Normalized count data: X, spliced, unspliced.
Extracted 2000 highly variable genes.
Logarithmized X
>>> scv.pp.moments(adata, n_neighbors=30, n_pcs=30)  
computing neighbors
    finished (0:00:13)
computing moments based on connectivities
    finished (0:00:00)
>>> scv.tl.velocity(adata, vkey='steady_state_velocity', mode='steady_state')  
computing velocities
    finished (0:00:00)
>>> scv.tl.velocity_graph(adata, vkey='steady_state_velocity')  
computing velocity graph (using 1/56 cores)
    finished (0:00:04)
>>> scv.tl.recover_dynamics(adata)  
recovering dynamics (using 1/56 cores)
    finished (0:06:25)
>>> scv.tl.velocity(adata, mode='dynamical', vkey='dynamical_velocity')  
computing velocities
    finished (0:00:02)
>>> scv.tl.velocity_graph(adata, vkey='dynamical_velocity', variance_stabilization=True)  
computing velocity graph (using 1/56 cores)
    finished (0:00:08)
>>> scv.pl.velocity_embedding_stream(adata, vkey='dynamical_velocity')  
computing velocity embedding
    finished (0:00:08)

[user@cn0911 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$