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scFates provides an extensive toolset for analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at… Click to show full abstract
scFates provides an extensive toolset for analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into scanpy ecosystem for seamless analysis of trajectories from single cell data of various modalities (e.g. RNA, ATAC). Availability and implementation scFates is released as open-source software under the BSD 3-Clause “New” License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on Github at https://github.com/LouisFaure/scFates/ Supplementary information A supplementary document is provided with a complete explanation of the underlying statistics, and two figures showing examples of analysis.
Journal Title: Bioinformatics
Year Published: 2022
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