Diffusion pseudotime robustly reconstructs lineage branching

By Laleh Haghverdi, Maren Büttner, F. Alexander Wolf, Florian Buettner, Fabian J Theis. 2016

1,334 citations recorded by the source corpus.

The temporal order of differentiating cells is intrinsically encoded in their single-cell expression profiles. We describe an efficient way to robustly estimate this order according to diffusion pseudotime (DPT), which measures transitions between cells using diffusion-like random walks. Our DPT software implementations make it possible to reconstruct the developmental progression of cells and identify transient or metastable states, branching decisions and differentiation endpoints.

Subfields: Molecular Biology

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Citations in the corpus

Prioritizing network communities (2018, methodology): “The data set was represented as a weighted graph of nearest neighbor relations (edges) among cells (nodes), where relations indicated cells with similar gene expression patterns calculated using diffusion pseudotime analysis 30 .”
CellRank for directed single-cell fate mapping (2020, positive): “This is essentially the transition matrix used in the diffusion pseudotime (DPT) 7 algorithm.”
A comparison of single-cell trajectory inference methods (2019, methodology): “Many attempts at tackling this issue have already been made 7,22,25,28–33 , but a comprehensive comparison of TI methods across a large number of different datasets is still lacking.”
Deep learning and alignment of spatially-resolved single cell transcriptomes with Tangram (2021, positive): “We trained Tangram using 253 MERFISH genes (all genes but one were detected in our snRNA-seq data).”
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics (2020, methodology): “Existing methods attempt to infer a trajectory within one timepoint (Haghverdi et al., 2016; Saelens et al., 2019; La Manno et al., 2018), or interpolate linearly between two timepoints (Yang & Uhler, 2019; Schiebinger et al.”
Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome (2017, methodology): “In this regard, SCENT differs substantially from other single-cell algorithms like Monocle 14 , MPath 15 , SCUBA 16 , Diffusion Pseudotime 17 or StemID 18 , in that it uses single-cell entropy to independently order single cells in pseudo-time (that is, differentiation potency), without the need for”
Visualizing structure and transitions in high-dimensional biological data (2019, methodology): “…end points, the remaining points are assigned to branches between two branch points or between a branch point and an end point using an approach that is based on a previously developed branch-point-detection method 14 , which compares the correlation and anticorrelation of neighborhood distances.”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “Chief among them are pseudotime-based methods (Bendall et al., 2014; Haghverdi et al., 2016; Saelens et al., 2019; Trapnell et al., 2014) first developed to infer the order of biological progression by learning a graph manifold of single cells based on transcriptome similarity.”
Normalizing single-cell RNA sequencing data: Challenges and opportunities (2017, methodology): “A second strategy for normalizing scRNA-seq datasets is to use generic methods that yield normalized expression measures that can be used as input in any subsequent analyses (e.g. [32–34]).”