CellRank is a modular framework to study cellular dynamics based on Markov state modeling of multi-view single-cell data. CellRank scales to large cell numbers, is fully compatible with the scverse ecosystem, and is easy to use. In the backend, it is powered by the pyGPCCA package.

References:

• Marius Lange, Volker Bergen, Michal Klein, Manu Setty, Bernhard Reuter, Mostafa Bakhti, Heiko Lickert, Meshal Ansari, Janine Schniering, Herbert B. Schiller, Dana Pe'er and Fabian J. Theis
  CellRank for directed single-cell fate mapping
  Nature Methods 19, pages 159–170 (2022).
• Philipp Weiler, Marius Lange, Michal Klein, Dana Pe’er, Fabian J. Theis
  Unified fate mapping in multiview single-cell data
  bioRxiv, doi: https://doi.org/10.1101/2023.07.19.549685.

• CellRank Home page

• Module Name: cellrank (see the modules page for more information)
• Unusual environment variables set
  • CELLRANK_HOME  installation directory
  • CELLRANK_BIN       executable directory
  • CELLRANK_SRC       source code directory
  • CELLRANK_DATA  sample data directory

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

[user@biowulf]$ sinteractive --mem=4g
[user@cn0911 ~]$module load scdrs   
[+] Loading singularity  3.10.5  on cn4185
[+] Loading cellrank 2.0.0
[user@cn0911 ~]$mkdir /data/$USER/cellrank && cd /data/$USER/cellrank
[user@cn0911 ~]$python-cr 
>>> import sys
>>> import cellrank as cr
>>> import scanpy as sc
>>> sc.settings.set_figure_params(frameon=False, dpi=100)
>>> cr.settings.verbosity = 2
>>> import warnings
>>> warnings.simplefilter("ignore", category=UserWarning)
>>> adata = cr.datasets.pancreas()
100%|█████████████████████████████████████████████████████████| 33.5M/33.5M [00:01<00:00, 18.7MB/s]
>>> adata
AnnData object with n_obs × n_vars = 2531 × 27998
    obs: 'day', 'proliferation', 'G2M_score', 'S_score', 'phase', 'clusters_coarse', 'clusters', 'clusters_fine', 'louvain_Alpha', 'louvain_Beta', 'palantir_pseudotime'
    var: 'highly_variable_genes'
    uns: 'clusters_colors', 'clusters_fine_colors', 'day_colors', 'louvain_Alpha_colors', 'louvain_Beta_colors', 'neighbors', 'pca'
    obsm: 'X_pca', 'X_umap'
    layers: 'spliced', 'unspliced'
    obsp: 'connectivities', 'distances'
>>> import scvelo as scv
>>> scv.pl.proportions(adata)

>>> scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000, subset_highly_variable=False)
Normalized count data: X, spliced, unspliced.
Extracted 2000 highly variable genes.
Logarithmized X.
>>> sc.tl.pca(adata)
[user@cn0911 ~]$sc.pp.neighbors(adata, n_pcs=30, n_neighbors=30, random_state=0) 
[user@cn0911 ~]$scv.pp.moments(adata, n_pcs=None, n_neighbors=None) 
computing moments based on connectivities
    finished (0:00:01) --> added
    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)
[user@cn0911 ~]$sc.pl.embedding(adata, basis="umap", color=["clusters", "proliferation"])

[user@cn0911 ~]$exit End the interactive session:

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