FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
By J. S. Herman, Sagar, Dominic Grün. Nature Methods, 2017
304 citations recorded by the source corpus.
To understand stem cell differentiation along multiple lineages, it is necessary to resolve heterogeneous cellular states and the ancestral relationships between them. We developed a robotic miniaturized CEL-Seq2 implementation to carry out deep single-cell RNA-seq of ∼2,000 mouse hematopoietic progenitors enriched for lymphoid lineages, and used an improved clustering algorithm, RaceID3, to identify cell types. To resolve subtle transcriptome differences indicative of lineage biases, we developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias in progenitor populations. Here we used FateID to delineate domains of fate bias and enable the derivation of high-resolution differentiation trajectories, thereby revealing a common progenitor population of B cells and plasmacytoid dendritic cells, which we validated by in vitro differentiation assays. We expect that FateID will improve understanding of the process of cell fate choice in complex multi-lineage differentiation systems.
Subfields: Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/bd81f89fc328d017949304d63bde50034ace2b66
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
Citations in the corpus
- Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming (2019, unknown): “These behaviors are likely due to the fact that FateID does not leverage time- course information in its present formulation.”
- CellRank for directed single-cell fate mapping (2020, unknown): “To evaluate the impact of including velocity information, we benchmarked CellRank with similarity-based methods that provide cell-fate probabilities (Palantir 21 , STEMNET 50 and FateID 51 ) and a velocity-based method that computes initial/terminal states (velocyto 14 ) on the pancreas data (Supple”
- Embracing the dropouts in single-cell RNA-seq analysis (2020, background): “Co-occurrence clustering identi fi ed eight clusters (30, 29, 60, 59, 46, 23, 37, and 24) corresponding to NPCs.”
- Characterization of cell fate probabilities in single-cell data with Palantir (2019, unknown): “Monocle 2 17 and FateID 37 (using RaceID clustering) fail to generate a coherent map of hematopoiesis (Supplementary Figs.”