CellRank 2.0.2

Default Boolean Operator: Terms in a search string will be connected by the AND operator, EXAMPLE: searching genomic melanoma will produce the same results as genomic AND melanoma .
AND, OR, NOT: Combine terms in a search with the boolean operators AND (to add specificity), OR (to broaden), or NOT (to eliminate a term). Operators must be capitalized (all other search terms are not case-sensitive).
Wild Card: Add wild card symbols * (open-ended) and ? (single character) to truncate terms. EXAMPLE: searching gen* shows results for 'gene', 'genetic', 'genomic, etc. Searching gene? returns results for 'gene' or 'genes' but not for 'genome'.
Phrase Searching: Enclose search in quotation marks "keyword or search term" to search for an exact phrase. EXAMPLE: "transcription factors" returns results for records where transcription is immediately followed by the word factors .
Diacritics: Words containing diacritics translate those characters to non-diacritic form. EXAMPLE: massague returns results for Massague&#769 .
Filter by: Located on the homepage (left panel), use filters to search for records or narrow the search. From search results page, filters can be removed by clicking on the term at the top of the page.

UID: 11372

Author(s): Lange, Marius, Weiler, Philipp, Palla, Giovanni, Heumos, Lukas, Schauner, Robert

Description: Description from GitHub:

"CellRank is a modular framework to study cellular dynamics based on Markov state modeling of multi-view single-cell data. See our documentation, and the CellRank 1 and CellRank 2 manuscript to learn more. See here for how to properly cite our work.

CellRank scales to large cell numbers, is fully compatible with the scverse ecosystem, and easy to use. In the backend, it is powered by pyGPCCA (Reuter et al. (2018)). Feel free to open an issue or send us an email if you encounter a bug, need our help or just want to make a comment/suggestion."

Subject(s)

Access via Zenodo

Accession #: 10210197

Access via GitHub
Access Restrictions: Free to All
Access Instructions: Available from Github under a BSD 3-Clause License. Available from Zenodo under a Creative Commons Attribution 4.0 International License.
DOI: 10.5281/zenodo.10210196
Associated Publications: Weiler P, Lange M, Klein M, Pe’er D, Theis F. CellRank 2: Unified fate mapping in multiview single-cell data. Nature Methods. 2024; 21(7):1196-1205.
Software Used: pyGPCCA
Generalized Perron Cluster Cluster Analysis package to coarse-grain reversible and non-reversible Markov state models
Dataset Format(s): PNG, zip, PY, RST, CSS
Dataset Size: 15.4 MB

Do you have or know of a dataset that should be added to the catalog? Let us know!