BioinformaticsBioinformaticsPeer-reviewed journal article
scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single cell data
Louis FaureiD, Ruslan SoldatoviD, Peter V. KharchenkoiD, Igor I. AdameykoiD
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 implementationscFates 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 informationA supplementary document is provided with a complete explanation of the underlying statistics, and two figures showing examples of analysis.
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: trajectory inference
- research focus: differential expression
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 117
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