Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/agitter/single-cell-pseudotime
An overview of algorithms for estimating pseudotime in single-cell RNA-seq data
https://github.com/agitter/single-cell-pseudotime
bioinformatics pseudotime-algorithms rna-seq single-cell
Last synced: 23 days ago
JSON representation
An overview of algorithms for estimating pseudotime in single-cell RNA-seq data
- Host: GitHub
- URL: https://github.com/agitter/single-cell-pseudotime
- Owner: agitter
- License: cc-by-4.0
- Created: 2016-06-25T20:38:57.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-06-03T14:03:58.000Z (29 days ago)
- Last Synced: 2024-06-03T16:19:47.092Z (29 days ago)
- Topics: bioinformatics, pseudotime-algorithms, rna-seq, single-cell
- Homepage:
- Size: 312 KB
- Stars: 386
- Watchers: 32
- Forks: 59
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome_single_cell - agitter's Pseudotime estimation list - An overview of algorithms for estimating pseudotime in single-cell RNA-seq data. (Similar lists and collections / Methods comparisons)
- awesome-single-cell - agitter's Pseudotime estimation list - An overview of algorithms for estimating pseudotime in single-cell RNA-seq data. (Similar lists and collections / Methods comparisons)
- awesome-single-cell - agitter's Pseudotime estimation list - An overview of algorithms for estimating pseudotime in single-cell RNA-seq data. (Similar lists and collections / Methods comparisons)
README
# Single-cell RNA-seq pseudotime estimation algorithms
[](https://github.com/agitter/single-cell-pseudotime/actions/workflows/links.yml)
[](https://doi.org/10.5281/zenodo.1297422)
Single cells, many algorithms.
The goal of this page is to catalog the many algorithms that estimate pseudotimes for cells based on their gene expression levels.
This problem is also referred to as single-cell trajectory inference or ordering.
It contains method names, software links, and manuscript links and simply seeks to list as many methods as possible without commentary.
Some related methods not specifically designed for RNA-seq (e.g. mass cytometry) are included as well, as are some methods for estimating RNA velocity.
The list also includes methods that are specifically designed to take pseudotemporal data as input.
The initial list was created by Anthony Gitter, but pull requests are very welcome.
Thank you to the other [contributors](https://github.com/agitter/single-cell-pseudotime/graphs/contributors).
## Citation
Anthony Gitter. Single-cell RNA-seq pseudotime estimation algorithms. 2018.
doi:10.5281/zenodo.1297422
https://github.com/agitter/single-cell-pseudotime
## Problem overview
Informally, the pseudotime estimation problem can be stated as:
- **Given:** single-cell gene expression measurements for a heterogeneous collection of cells that is transitioning from biological state **A** to state **B**
- **Return:** a quantitative value for each cell that represents its progress in the **A** to **B** transition
There are many ways to approach this problem, and major algorithmic steps that are common to most (but not all) methods are:
- Reduce the dimension of the dataset
- Find a smooth progression through the low dimensional data, assuming that cells that are nearby one another in the low dimensional space have similar expression levels because they are at similar points in to **A** to **B** process
Dimension reduction sometimes relies on knowledge of important marker genes and sometimes uses the full gene expression matrix.
The trajectory through the low dimensional space can be identified using graph algorithms (e.g., minimum spanning tree or shortest path), principal curves, or probabilistic models (e.g., Gaussian process).
[Bacher and Kendziorski 2016](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y), [Trapnell 2015](http://genome.cshlp.org/content/25/10/1491.full), [Tanay and Regev 2017](http://www.nature.com/nature/journal/v541/n7637/full/nature21350.html), [Moon et al. 2017](https://doi.org/10.1016/j.coisb.2017.12.008), [Tritschler et al. 2019](https://doi.org/10.1242/dev.170506), [Weiler et al. 2021](https://doi.org/10.1101/2021.12.22.473434), and [Ding et al. 2022](https://doi.org/10.1038/s41576-021-00444-7) provide a more comprehensive overview of single-cell RNA-seq and the pseudotime estimation problem.
[Cannoodt et al. 2016](http://onlinelibrary.wiley.com/wol1/doi/10.1002/eji.201646347/abstract) reviews pseudotime inference algorithms.
[Pablo Cordero's blog post](http://hyperparameter.space/blog/a-single-cell-journey-from-mechanistic-to-descriptive-modeling-and-back-again/) discusses why it is hard to recover the correct dynamics of a system from single-cell data.
For more general lists of methods for single-cell RNA-seq see [seandavi/awesome-single-cell](https://github.com/seandavi/awesome-single-cell) and [scRNA-tools](https://www.scrna-tools.org/).
The Hemberg lab [single-cell RNA-seq course](https://scrnaseq-course.cog.sanger.ac.uk/website/index.html) has an [overview of five pseudotime algorithms](https://scrnaseq-course.cog.sanger.ac.uk/website/biological-analysis.html#pseudotime-analysis) with usage examples.
Many modern ideas for pseudotime estimation are descended from [Magwene et al. 2003](https://doi.org/10.1093/bioinformatics/btg081) on reconstructing the order of microarray expression samples.
Single-cell expression data have also inspired new methods for gene regulatory network reconstruction, as reviewed by [Fiers et al. 2018](https://doi.org/10.1093/bfgp/elx046) and [Todorov et al. 2018](https://doi.org/10.1007/978-1-4939-8882-2_10).
Several of these, such as [SINGE](http://doi.org/10.1016/j.celrep.2022.110333), treat pseudotime annotations as time points and extend traditional time series network inference algorithms for single-cell data.
[BEELINE](http://doi.org/10.1038/s41592-019-0690-6), [SERGIO](http://doi.org/10.1016/j.cels.2020.08.003), and [McCalla et al. 2023](https://doi.org/10.1093/g3journal/jkad004) benchmark many of these specialized network inference methods.
## Choosing a method
Some of the distinguishing factors among algorithms include:
- Use of prior knowledge such as capture times (DeLorean) or switch-like marker genes (Ouija)
- Modeling specific types of biological processes such as branching processes in differentiation (multiple methods) or cyclic processes (Oscope)
- Return a single pseudotime or a posterior distribution over pseudotimes for each cell
- Perform additional analyses after inferring pseudotimes such as regulatory network inference or identifying differentially expressed genes over pseudotime
[Saelens et al. 2019](https://doi.org/10.1038/s41587-019-0071-9) performed a comprehensive evaluation of 29 different single-cell trajectory inference methods and discuss the different types of algorithms in more detail.
They benchmark both quantitative performance and assess software quality.
See their [website](https://dynverse.org/) and [GitHub repository](https://github.com/dynverse/dynverse) as well.
[Tian et al. 2018](https://doi.org/10.1101/433102) also include trajectory inference algorithms in their single-cell RNA-seq benchmarking study.
[Escort](https://doi.org/10.1101/2023.12.18.572214) is a framework to help guide the selection of a suitable trajectory inference algorithm for a dataset.
## Algorithms
### Temporal Reconstruction Algorithm
Manuscript: [Reconstructing the temporal ordering of biological samples using microarray data](https://doi.org/10.1093/bioinformatics/btg081)
### Monocle / Monocle 2 / Monocle 3 / Census
Software: https://bioconductor.org/packages/release/bioc/html/monocle.html
Monocle manuscript: [The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells](https://doi.org/10.1038/nbt.2859)
Census manuscript: [Single-cell mRNA quantification and differential analysis with Census](https://doi.org/10.1038/nmeth.4150)
Monocle 2 manuscript: [Reversed graph embedding resolves complex single-cell trajectories](https://doi.org/10.1038/nmeth.4402)
Monocle 3 manuscript: [The single-cell transcriptional landscape of mammalian organogenesis](https://doi.org/10.1038/s41586-019-0969-x)
### Wanderlust / Cycler / Wishbone
Wanderlust software: http://www.c2b2.columbia.edu/danapeerlab/html/wanderlust.html
Wanderlust manuscript: [Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development](https://doi.org/10.1016/j.cell.2014.04.005)
Cycler manuscript: [Trajectories of cell-cycle progression from fixed cell populations](https://doi.org/10.1038/nmeth.3545)
Wishbone software: http://www.c2b2.columbia.edu/danapeerlab/html/wishbone.html
Wishbone manuscript: [Wishbone identifies bifurcating developmental trajectories from single-cell data](https://doi.org/10.1038/nbt.3569)
### SCUBA
Software: https://github.com/gcyuan/SCUBA
Manuscript: [Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape](https://doi.org/10.1073/pnas.1408993111)
### Oscope
Software: https://www.biostat.wisc.edu/~kendzior/OSCOPE/
Manuscript: [Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments](https://doi.org/10.1038/nmeth.3549)
### Diffusion maps / destiny
destiny software: http://bioconductor.org/packages/release/bioc/html/destiny.html
Diffusion maps manuscript (a): [Decoding the regulatory network of early blood development from single-cell gene expression measurements](https://doi.org/10.1038/nbt.3154)
Diffusion maps manuscript (b): [Diffusion maps for high-dimensional single-cell analysis of differentiation data](https://doi.org/10.1093/bioinformatics/btv325)
destiny manuscript: [destiny: diffusion maps for large-scale single-cell data in R](https://doi.org/10.1093/bioinformatics/btv715)
### DeLorean
Software: https://github.com/JohnReid/DeLorean
Manuscript: [Pseudotime estimation: deconfounding single cell time series](https://doi.org/10.1093/bioinformatics/btw372)
### Waterfall
Manuscript: [Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis](https://doi.org/10.1016/j.stem.2015.07.013)
### Embeddr
Software: https://github.com/kieranrcampbell/embeddr
Manuscript: [Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles](https://doi.org/10.1101/027219)
### GP-LVM and pseudogp
GP-LVM software: https://github.com/kieranrcampbell/gpseudotime
GP-LVM manuscript: [Bayesian Gaussian Process Latent Variable Models for pseudotime inference in single-cell RNA-seq data](https://doi.org/10.1101/026872)
pseudogp software: https://github.com/kieranrcampbell/pseudogp
pseudogp manuscript: [Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference](https://doi.org/10.1371/journal.pcbi.1005212)
### GP-LVM
Analysis code: https://github.com/Teichlab/spectrum-of-differentiation-supplements
Manuscript: [Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells](https://doi.org/10.1016/j.celrep.2015.12.082)
### SLICER
Software: https://github.com/jw156605/SLICER
Manuscript: [SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data](https://doi.org/10.1186/s13059-016-0975-3)
### TSCAN
Software: https://github.com/zji90/TSCAN
Manuscript: [TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis](https://doi.org/10.1093/nar/gkw430)
### SCOUP
Software: https://github.com/hmatsu1226/SCOUP
Manuscript: [SCOUP: a probabilistic model based on the Ornstein–Uhlenbeck process to analyze single-cell expression data during differentiation](https://doi.org/10.1186/s12859-016-1109-3)
### Topslam
Software: https://github.com/mzwiessele/topslam
Manuscript: [Topslam: Waddington Landscape Recovery for Single Cell Experiments
](https://doi.org/10.1101/057778)
### Ouija
Software: https://github.com/kieranrcampbell/ouija and http://www.github.com/kieranrcampbell/ouijaflow
Manuscript: [A descriptive marker gene approach to single-cell pseudotime inference](https://doi.org/10.1093/bioinformatics/bty498)
### CellTrails
Sofware: https://bioconductor.org/packages/release/bioc/html/CellTrails.html
Manuscript: [Transcriptional dynamics of hair-bundle morphogenesis revealed with CellTrails](https://doi.org/10.1016/j.celrep.2018.05.002)
### Slingshot
Software: https://github.com/kstreet13/slingshot
Extended vignette: https://github.com/drisso/bioc2016singlecell/tree/master/vignettes
Manuscript: [Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics](https://doi.org/10.1101/128843)
Workflow manuscript: [Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference](https://doi.org/10.12688/f1000research.12122.1)
### GPfates
Software: https://github.com/Teichlab/GPfates
Manuscript: [Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation](https://doi.org/10.1101/074971)
### SCIMITAR
Software: https://github.com/dimenwarper/scimitar
Manuscript: [Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories](https://doi.org/10.1101/070151)
### WaveCrest
Software: https://github.com/lengning/WaveCrest
Manuscript: [Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm](https://doi.org/10.1186/s13059-016-1033-x)
### CellTree
Software: http://bioconductor.org/packages/release/bioc/html/cellTree.html
Manuscript: [CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data](https://doi.org/10.1186/s12859-016-1175-6)
### Bayesian hierarchical mixture of factor analysers (MFA)
Software: http://www.github.com/kieranrcampbell/mfa
Manuscript: [Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers](http://doi.org/10.12688/wellcomeopenres.11087.1)
### Mpath
Software: https://github.com/JinmiaoChenLab/Mpath
Manuscript: [Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development](https://doi.org/10.1038/ncomms11988)
### SCORPIUS
Software: https://github.com/rcannood/SCORPIUS
Manuscript: [SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development](https://doi.org/10.1101/079509)
### SCODE
Software: https://github.com/hmatsu1226/SCODE
Manuscript: [SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation](https://doi.org/10.1093/bioinformatics/btx194)
### switchde
Software: https://bioconductor.org/packages/release/bioc/html/switchde.html
Manuscript: [switchde: inference of switch-like differential expression along single-cell trajectories](https://doi.org/10.1093/bioinformatics/btw798)
### MAGIC
Software: https://github.com/pkathail/magic/
Manuscript: [MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data](https://doi.org/10.1101/111591)
### PHATE
Software: https://github.com/KrishnaswamyLab/PHATE
Manuscript: [Visualizing Transitions and Structure for High Dimensional Data Exploration
](https://doi.org/10.1101/120378)
### SOMSC
Manuscript: [SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions](https://doi.org/10.1101/124693)
### TASIC
Software: https://www.andrew.cmu.edu/user/sabrinar/TASIC
Manuscript: [TASIC: determining branching models from time series single cell data](https://doi.org/10.1093/bioinformatics/btx173)
### FORKS
Software: https://github.com/macsharma/FORKS
Manuscript: [FORKS: Finding Orderings Robustly using K-means and Steiner trees](https://doi.org/10.1101/132811)
### UNCURL
Software: https://github.com/yjzhang/uncurl_python and https://github.com/mukhes3/UNCURL_release
Manuscript: [Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge
](https://doi.org/10.1101/142398)
### reCAT
Software: https://github.com/tinglab/reCAT
Manuscript: [Reconstructing cell cycle pseudo time-series via single-cell transcriptome data](https://doi.org/10.1038/s41467-017-00039-z)
### PhenoPath
Software: [Bioconductor package](https://doi.org/10.18129/B9.bioc.phenopath) and https://github.com/kieranrcampbell/phenopath
Manuscript: [Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data](https://doi.org/10.1038/s41467-018-04696-6)
### Branched Gaussian processes
Software: https://github.com/ManchesterBioinference/BranchedGP
Manuscript: [BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process](https://doi.org/10.1186/s13059-018-1440-2)
### Branch-recombinant Gaussian Processes
Software: https://github.com/cap76/BranchingGPs
Manuscript: [Nonparametric Bayesian inference of transcriptional branching and recombination identifies regulators of early human germ cell development](https://doi.org/10.1101/167684)
### MATCHER
Software: https://github.com/jw156605/MATCHER and https://pypi.python.org/pypi/pymatcher
Manuscript: [MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics](https://doi.org/10.1186/s13059-017-1269-0)
### SoptSC
Software: https://github.com/WangShuxiong/SoptSC
Manuscript: [Low-rank Similarity Matrix Optimization Identifies Subpopulation Structure and Orders Single Cells in Pseudotime](https://doi.org/10.1101/168922)
### Di-SNE
Manuscript: [Assessment of clonal kinetics reveals multiple trajectories of dendritic cell development](https://doi.org/10.1101/167635)
### Population Balance Analysis
Software: https://github.com/AllonKleinLab/PBA
Manuscript: [Fundamental limits on dynamic inference from single cell snapshots](https://doi.org/10.1101/170118)
### Scanpy
Software: https://github.com/theislab/scanpy and https://pypi.python.org/pypi/scanpy
Manuscript: [Scanpy for analysis of large-scale single-cell gene expression data](https://doi.org/10.1101/174029)
### TIDES
Software: https://github.com/roshan9128/tides
Manuscript: [Learning Edge Rewiring in EMT from Single Cell Data
](https://doi.org/10.1101/155028)
### WADDINGTON-OT
Software: https://pypi.org/project/wot/
Manuscript: [Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming](https://doi.org/10.1101/191056)
### pseudodynamics
Software: https://github.com/theislab/pseudodynamics
Manuscript: [Inferring population dynamics from single-cell RNA-sequencing time series data](https://doi.org/10.1038/s41587-019-0088-0)
### Partition-based graph abstraction
Software: https://github.com/theislab/paga
Manuscript: [PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells](https://doi.org/10.1186/s13059-019-1663-x)
### GPseudoRank
Software: https://github.com/magStra/GPseudoRank
Manuscript: [GPseudoRank: MCMC for sampling from posterior distributions of pseudo-orderings using Gaussian processes](https://doi.org/10.1093/bioinformatics/bty664)
### FateID
Software: https://github.com/dgrun/FateID
Manuscript: [FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data](https://doi.org/10.1101/218115)
### cycleX
Manuscript: [cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors](https://doi.org/10.1101/222372)
### GrandPrix
Software: https://github.com/ManchesterBioinference/GrandPrix
Manuscript: [GrandPrix: Scaling up the Bayesian GPLVM for single-cell data](https://doi.org/10.1101/227843)
### Partial differential equations
Manuscript: [Modeling acute myeloid leukemia in a continuum of differentiation states](https://doi.org/10.1101/237438)
### scdiff
Software: https://github.com/phoenixding/scdiff and https://pypi.python.org/pypi/scdiff/
Manuscript: [Reconstructing differentiation networks and their regulation from time series single cell expression data](https://doi.org/10.1101/gr.225979.117)
### Topographer
Manuscript: [Topographer Reveals Stochastic Dynamics of Cell Fate Decisions from Single-Cell RNA-Seq Data](https://doi.org/10.1101/251207)
### Markov-Chain Entropy
Manuscript: [Quantifying Waddington's epigenetic landscape: a comparison of single-cell potency measures](https://doi.org/10.1093/bib/bby093)
### Microstates
Manuscript: [Machine learning methods to reverse engineer dynamic gene regulatory networks governing cell state transitions](https://doi.org/10.1101/264671)
### DensityPath
Software: https://github.com/ucasdp/DensityPath
Manuscript: [DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data](https://doi.org/10.1101/276311)
### STREAM
Software: https://github.com/pinellolab/stream
Manuscript: [STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data](https://doi.org/10.1101/302554)
Website: http://stream.pinellolab.partners.org/
### HopLand
Software: https://github.com/NetLand-NTU/HopLand
Manuscript: [HopLand: single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddington's epigenetic landscape](https://doi.org/10.1093/bioinformatics/btx232)
### Dynamic Distribution Decomposition
Manuscript: [Dynamic Distribution Decomposition for Single-Cell Snapshot Time Series Identifies Subpopulations and Trajectories during iPSC Reprogramming](https://doi.org/10.1101/367789)
### Continuous state HMMs
Software: https://github.com/jessica1338/CSHMM-for-time-series-scRNA-Seq
Manuscript: [Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data](https://doi.org/10.1101/380568)
### Palantir
Software: https://github.com/dpeerlab/Palantir/
Manuscript: [Palantir characterizes cell fate continuities in human hematopoiesis](https://doi.org/10.1038/s41587-019-0068-4)
### Trajectory inference Based on SNP information
Software: https://github.com/phoenixding/tbsp
Manuscript: [Cell lineage inference from SNP and scRNA-Seq data](https://doi.org/10.1101/401943)
### t-Distributed Gaussian Process Latent Variable Model
Software: https://github.com/architverma1/tGPLVM
Manuscript: [A robust nonlinear low-dimensional manifold for single cell RNA-seq data](https://doi.org/10.1101/443044)
### Sinova
Software: https://github.com/bionova/sinova
Manuscript: [Systematic Reconstruction of Molecular Cascades Regulating GP Development Using Single-Cell RNA-Seq](https://doi.org/10.1016/j.celrep.2016.04.043)
### Lineage tracing on transcriptional landscapes
Software: Multiple repositories
Manuscript: [Lineage tracing on transcriptional landscapes links state to fate during differentiation](https://doi.org/10.1101/467886)
### CALISTA
Software: https://github.com/CABSEL/CALISTA
Manuscript: [CALISTA: Clustering And Lineage Inference in Single-Cell Transcriptional Analysis](https://doi.org/10.1101/257550)
### TRACER
Manuscript: [Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution](https://doi.org/10.1101/570341)
### psupertime
Software: https://github.com/wmacnair/psupertime
Manuscript: [psupertime: supervised pseudotime inference for single cell RNA-seq data with sequential labels](https://doi.org/10.1101/622001)
### OscoNet
Software: https://github.com/alexisboukouvalas/OscoNet
Manuscript: [OscoNet: Inferring oscillatory gene networks](https://doi.org/10.1101/600049)
### Cyclum
Software: https://github.com/KChen-lab/cyclum
Manuscript: [Latent periodic process inference from single-cell RNA-seq data](https://doi.org/10.1101/625566)
### MERLoT
Software: https://github.com/soedinglab/merlot
Manuscript: [Reconstructing complex lineage trees from scRNA-seq data using MERLoT](https://doi.org/10.1093/nar/gkz706)
### scVelo
Software: https://scvelo.org
Manuscript: [Generalizing RNA velocity to transient cell states through dynamical modeling](https://doi.org/10.1038/s41587-020-0591-3)
### Tempora
Software: https://github.com/BaderLab/Tempora
Manuscript: [Tempora: cell trajectory inference using time-series single-cell RNA sequencing data](https://doi.org/10.1101/846907)
### CellCycleTRACER
Software: https://ibm.biz/cellcycletracer-aas
Manuscript: [CellCycleTRACER accounts for cell cycle and volume in mass cytometry data](https://doi.org/10.1038/s41467-018-03005-5)
### TrajectoryNet
Software: https://github.com/krishnaswamylab/TrajectoryNet and https://github.com/KrishnaswamyLab/Cell-Dynamics-Pipeline
Manuscript: [TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics](https://arxiv.org/abs/2002.04461)
Additional manuscript: [Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport](https://doi.org/10.1101/2023.03.28.534644)
### redPATH
Software: https://github.com/tinglab/redPATH
Manuscript: [redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer](https://doi.org/10.1101/2020.03.05.977686)
### GraphDR and StructDR
Software: https://github.com/jzthree/quasildr
Manuscript: [An analytical framework for interpretable and generalizable 'quasilinear' single-cell data analysis](https://doi.org/10.1101/2020.04.12.022806)
### Pseudocell Tracer
Software: https://github.com/akds/pseudocell
Manuscript: [Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer](https://doi.org/10.1101/2020.06.26.173179)
### TinGa
Software: https://github.com/Helena-todd/TinGa
Manuscript: [TinGa: fast and flexible trajectory inference with Growing Neural Gas ](https://doi.org/10.1093/bioinformatics/btaa463)
### scDEC
Software: https://github.com/kimmo1019/scDEC
Manuscript: [Simultaneous deep generative modeling and clustering of single cell genomic data](https://doi.org/10.1101/2020.08.17.254730)
### VeTra
Software: https://github.com/wgzgithub/VeTra
Manuscript: [VeTra: a new trajectory inference tool based on RNA velocity](https://doi.org/10.1101/2020.09.01.277095)
### DTFLOW
Software: https://github.com/statway/DTFLOW
Manuscript: [DTFLOW: Inference and Visualization of Single-cell Pseudo-temporal Trajectories Using Diffusion Propagation](https://doi.org/10.1101/2020.09.10.290973)
### CellPath
Software: https://github.com/PeterZZQ/CellPath
Manuscript: [Inference of multiple trajectories in single cell RNA-seq data from RNA velocity](https://doi.org/10.1101/2020.09.30.321125)
### CellRank / CellRank 2
Software: https://cellrank.org
CellRank manuscript: [CellRank for directed single-cell fate mapping](https://doi.org/10.1038/s41592-021-01346-6)
CellRank 2 manuscript: [Unified fate mapping in multiview single-cell data](https://doi.org/10.1101/2023.07.19.549685)
### Revelio
Software: https://github.com/danielschw188/Revelio
Manuscript: [The transcriptome dynamics of single cells during the cell cycle](https://doi.org/10.15252/msb.20209946)
### Cytopath
Software: https://github.com/aron0093/cytopath
Manuscript: [Cytopath: Simulation based inference of differentiation trajectories from RNA velocity fields](https://doi.org/10.1101/2020.12.21.423801)
### VIA
Software: https://github.com/ShobiStassen/VIA
Manuscript: [VIA: Generalized and scalable trajectory inference in single-cell omics data](https://doi.org/10.1101/2021.02.10.430705)
### Global Waddington-OT
Software: https://github.com/zsteve/gWOT
Manuscript: [Towards a mathematical theory of trajectory inference](https://arxiv.org/abs/2102.09204)
### StationaryOT
Software: https://github.com/zsteve/StationaryOT
Manuscript: [Optimal transport analysis reveals trajectories in steady-state systems](https://doi.org/10.1101/2021.03.02.433630)
### Condiments
Software: https://github.com/HectorRDB/condiments
Vignettes: https://hectorrdb.github.io/condimentsPaper/
Manuscript: [Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression](https://doi.org/10.1101/2021.03.09.433671)
### DeepCycle
Software: https://github.com/andreariba/DeepCycle
Manuscript: [Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning](https://doi.org/10.1101/2021.03.17.435887)
### Tricycle
Software: https://github.com/hansenlab/tricycle
Manuscript: [Universal prediction of cell cycle position using transfer learning](https://doi.org/10.1101/2021.04.06.438463)
### scShaper
Software: https://github.com/elolab/scshaper
Manuscript: [scShaper: ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data](https://doi.org/10.1101/2021.05.03.442435)
### CCPE
Software: https://github.com/LiuJJ0327/CCPE
Manuscript: [CCPE: Cell Cycle Pseudotime Estimation for Single Cell RNA-seq Data](https://doi.org/10.1101/2021.06.13.448263)
### scDVF
Software: https://github.com/gersteinlab/scDVF
Manuscript: [scDVF: Data-driven Single-cell Transcriptomic Deep Velocity Field Learning with Neural Ordinary Differential Equations](https://doi.org/10.1101/2022.02.15.480564)
### Tempo
Software: https://github.com/bauerbach95/tempo
Manuscript: [Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics](https://doi.org/10.1101/2022.03.15.484454)
### DeepVelo
Software: https://github.com/bowang-lab/DeepVelo
Manuscript: [DeepVelo: Deep Learning extends RNA velocity to multi-lineage systems with cell-specific kinetics](https://doi.org/10.1101/2022.04.03.486877)
### LRT
Software: https://github.com/JuanXie19/LRT
Manuscript: [LRT: T Cell Trajectory Inference by Integrative Analysis of Single Cell TCR-seq and RNA-seq data](https://doi.org/10.1101/2022.04.14.488320)
### scTour
Software: https://github.com/LiQian-XC/sctour
Manuscript: [scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics](https://doi.org/10.1101/2022.04.17.488600)
### UniTVelo
Software: https://github.com/StatBiomed/UniTVelo
Manuscript: [UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference](https://doi.org/10.1101/2022.04.27.489808)
### VITAE
Software: https://github.com/jaydu1/VITAE
Manuscript: [Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior](https://doi.org/10.1101/2020.12.26.424452)
### Real-time axis for T cells
Manuscript: [From pseudotime to true dynamics: reconstructing a real-time axis for T cells differentiation](https://doi.org/10.1101/2022.06.09.495431)
### GeneTrajectory
Software: https://github.com/KlugerLab/GeneTrajectory
Manuscript [Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics](https://doi.org/10.1101/2022.07.08.499404)
### scFates
Software: https://scfates.readthedocs.io/en/latest/
Manuscript: [scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single cell data](https://doi.org/10.1101/2022.07.09.498657)
### veloVI
Software: https://github.com/YosefLab/velovi
Manuscript: [Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells](https://doi.org/10.1101/2022.08.12.503709)
### MIRA
Software: https://github.com/cistrome/MIRA
Manuscript: [MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells](https://doi.org/10.1038/s41592-022-01595-z)
### Pyro-Velocity
Software: https://github.com/pinellolab/pyrovelocity
Manuscript: [Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data](https://doi.org/10.1101/2022.09.12.507691)
### Totem
Software: https://github.com/elolab/Totem
Manuscript: [Cell-connectivity-guided trajectory inference from single-cell data](https://doi.org/10.1093/bioinformatics/btad515)
### scLTNN
Software: https://github.com/Starlitnightly/scltnn
Manuscript: [Identify the origin and end cells and infer the trajectory of cellular fate automatically](https://doi.org/10.1101/2022.09.28.510020)
### UPMM
Manuscript: [Modeling Single-Cell Dynamics Using Unbalanced Parameterized Monge Maps](https://doi.org/10.1101/2022.10.04.510766)
### PhyloVelo
Software: https://github.com/kunwang34/PhyloVelo
Manuscript: [Cell division history encodes directional information of fate transitions](https://doi.org/10.1101/2022.10.06.511094)
### MultiVelo
Software: https://github.com/welch-lab/MultiVelo
Manuscript: [Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction](https://doi.org/10.1038/s41587-022-01476-y)
### SCTC
Software: https://github.com/hailinphysics/sctc
Manuscript: [SCTC: inference of developmental potential from single-cell transcriptional complexity](https://doi.org/10.1101/2022.10.14.512265)
### MIOFlow
Software: https://github.com/KrishnaswamyLab/MIOFlow
Manuscript: [Manifold Interpolating Optimal-Transport Flows for Trajectory Inference](https://openreview.net/forum?id=ahAEhOtVif)
### DEAPLOG
Software: https://github.com/ZhangHongbo-Lab/DEAPLOG
Manuscript: [A method for differential expression analysis and pseudo-temporal locating and ordering of genes in single-cell transcriptomic data](https://doi.org/10.1101/2022.12.21.521359)
### GCSTI
Software: https://github.com/xznhy/rna-seq
Manuscript: [GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression](https://doi.org/10.1109/TCBB.2023.3266109)
### moscot
Software: https://moscot-tools.org/
Manuscript: [Mapping cells through time and space with moscot](https://doi.org/10.1101/2023.05.11.540374)
### DELVE
Software: https://github.com/jranek/delve
Manuscript: [Feature selection for preserving biological trajectories in single-cell data](https://doi.org/10.1101/2023.05.09.540043)
### Velvet
Software: https://github.com/rorymaizels/velvet
Manuscript: [Deep dynamical modelling of developmental trajectories with temporal transcriptomics](https://doi.org/10.1101/2023.07.06.547989)
### ENTRAIN
Software: https://github.com/theimagelab/entrain
Manuscript: [ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor-ligand interactions that specify cell fate](https://doi.org/10.1101/2023.07.09.548284)
### TFvelo
Software: https://github.com/xiaoyeye/TFvelo
Manuscript: [TFvelo: gene regulation inspired RNA velocity estimation](https://doi.org/10.1101/2023.07.12.548785)
### TopGen
Manuscript: [Unraveling cell differentiation mechanisms through topological exploration of single-cell developmental trajectories](https://doi.org/10.1101/2023.07.28.551057)
### cell2fate
Software: https://github.com/BayraktarLab/cell2fate
Manuscript: [Model-based inference of RNA velocity modules improves cell fate prediction](https://doi.org/10.1101/2023.08.03.551650)
### FBSDE
Software: https://github.com/Diebrate/population_model
Manuscript: [Modeling Single Cell Trajectory Using Forward-Backward Stochastic Differential Equations](https://doi.org/10.1101/2023.08.10.552373)
### cy2path
Software: https://github.com/aron0093/cy2path
Manuscript: [Factorial state-space modelling for kinetic clustering and lineage inference](https://doi.org/10.1101/2023.08.21.554135)
### scEGOT
Software: https://github.com/yachimura-lab/scEGOT
Manuscript: [scEGOT: Single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport](https://doi.org/10.1101/2023.09.11.557102)
### Ricci flow
Manuscript: [Charting cellular differentiation trajectories with Ricci flow](https://doi.org/10.1101/2023.07.20.549833)
### Flat NB-VAE
Manuscript: [Modelling single-cell RNA-seq trajectories on a flat statistical manifold](https://openreview.net/pdf?id=sXRpvW3cRR)
### Sceptic
Software: https://github.com/Noble-Lab/Sceptic
Manuscript: [Pseudotime analysis for time-series single-cell sequencing and imaging data](https://doi.org/10.1101/2023.11.03.565575)
### VeloCycle
Software: https://github.com/lamanno-epfl/velocycle/
Manuscript: [Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations](https://doi.org/10.1101/2024.01.18.576093)
### Chronocell
Software: https://github.com/pachterlab/FGP_2024
Manuscript: [Trajectory inference from single-cell genomics data with a process time model](https://doi.org/10.1101/2024.01.26.577510)
### PSEUDOTIMEABC
Software: https://github.com/keita-iida/PSEUDOTIMEABC
Manuscript: [Identifying key regulatory genes in drug resistance acquisition: Modeling pseudotime trajectories of single-cell transcriptome](https://doi.org/10.1101/2024.04.25.591115)
### scTEP
Software: https://cran.r-project.org/package=scTEP
Manuscript: [A robust and accurate single-cell data trajectory inference method using ensemble pseudotime](https://doi.org/10.1186/s12859-023-05179-2)
### TrajAtlas
Software: https://github.com/GilbertHan1011/TrajAtlas
Manuscript: [Trajectory-centric Framework TrajAtlas reveals multi-scale differentiation heterogeneity among cells, genes, and gene module in osteogenesis](https://doi.org/10.1101/2024.05.28.596174)
## Related topics
### Cicero
Manuscript: [Chromatin accessibility dynamics of myogenesis at single cell resolution](https://doi.org/10.1101/155473)
### Effects of imputation on cell ordering
Manuscript: [Comparison of computational methods for imputing single-cell RNA-sequencing data](https://doi.org/10.1101/241190)
### PROSSTT
Software: https://github.com/soedinglab/prosstt
Manuscript: [PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes](https://doi.org/10.1101/256941)
### CellAlign
Software: https://github.com/shenorrLab/cellAlign
Manuscript: [Alignment of single-cell trajectories to compare cellular expression dynamics](https://doi.org/10.1038/nmeth.4628)
### CONFESS
Software: http://bioconductor.org/packages/release/bioc/html/CONFESS.html
Manuscript: [CONFESS: Fluorescence-based single-cell ordering in R](https://doi.org/10.1101/407932)
### Trajectory alignment
Software: https://www.cell.com/cms/10.1016/j.cels.2018.07.006/attachment/2b57ebff-a502-4819-b8ed-8a87d17a4ae7/mmc4.zip
Manuscript: [Aligning Single-Cell Developmental and Reprogramming Trajectories Identifies Molecular Determinants of Myogenic Reprogramming Outcome](https://doi.org/10.1016/j.cels.2018.07.006)
### ImageAEOT
Manuscript: [Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes](https://doi.org/10.1101/455469)
### RNA velocity
Software: http://velocyto.org/
Manuscript: [RNA velocity of single cells](https://doi.org/10.1038/s41586-018-0414-6)
### devMap
Manuscript: [High Resolution Comparison of Cancer-Related Developmental Processes Using Trajectory Alignment](https://doi.org/10.1101/469601)
### Trajan
Software: https://github.com/canzarlab/Trajan
Manuscript: [Dynamic pseudo-time warping of complex single-cell trajectories](https://doi.org/10.1101/522672)
### SINGE
Software: https://github.com/gitter-lab/SINGE
Manuscript: [Network inference with Granger causality ensembles on single-cell transcriptomics](https://doi.org/10.1016/j.celrep.2022.110333)
### GPseudoClust
Software: https://github.com/magStra/GPseudoClust
Manuscript: [GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution](https://doi.org/10.1101/567115)
### tradeSeq
Software: http://www.bioconductor.org/packages/release/bioc/html/tradeSeq.html
Manuscript: [Trajectory-based differential expression analysis for single-cell sequencing data](https://doi.org/10.1038/s41467-020-14766-3)
### CORGI
Software: https://github.com/YutongWangUMich/corgi
Manuscript: [A gene filter for comparative analysis of single-cell RNA-sequencing trajectory datasets](https://doi.org/10.1101/637488)
### BEELINE
Software: https://github.com/murali-group/Beeline
Manuscript: [Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data](https://doi.org/10.1101/642926)
### Dynamo
Software: https://github.com/aristoteleo/dynamo-release
Manuscript: [Mapping Vector Field of Single Cells](https://doi.org/10.1101/696724)
### SERGIO
Software: https://github.com/PayamDiba/SERGIO
Manuscript: [A single-cell expression simulator guided by gene regulatory networks](https://doi.org/10.1101/716811)
### GeneSwitches
Software: https://geneswitches.ddnetbio.com/
Manuscript: [GeneSwitches : Ordering gene-expression and functional events in single-cell experiments](https://doi.org/10.1101/832626)
### CAPITAL
Software: https://github.com/ykat0/capital
Manuscript: [Alignment of time-course single-cell RNA-seq data with CAPITAL](https://doi.org/10.1101/859751)
### Transcriptional uncertainty landscapes
Manuscript: [Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data](https://doi.org/10.1101/2020.04.23.056069)
### Pseudo-Location
Manuscript: [Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression](https://doi.org/10.1101/2020.06.11.145565)
### fishpond
Software: http://bioconductor.org/packages/devel/bioc/html/fishpond.html and https://github.com/skvanburen/scUncertaintyPaperCode
Manuscript: [Compression of quantification uncertainty for scRNA-seq counts](https://doi.org/10.1101/2020.07.06.189639)
### scHOT
Software: https://bioconductor.org/packages/scHOT
Manuscript: [Investigating higher-order interactions in single-cell data with scHOT](https://doi.org/10.1038/s41592-020-0885-x)
### PRESCIENT
Software: https://github.com/gifford-lab/prescient
Manuscript: [Generative modeling of single-cell population time series for inferring cell differentiation landscapes](https://doi.org/10.1101/2020.08.26.269332)
### Mathematics of RNA Velocity
Manuscript: [On the Mathematics of RNA Velocity I: Theoretical Analysis](https://doi.org/10.1101/2020.09.19.304584)
### Mathematics of RNA Velocity II
Manuscript: [On the Mathematics of RNA Velocity II: Algorithmic Aspects](https://doi.org/10.1101/2023.06.09.544270)
### PseudotimeDE
Software: https://github.com/SONGDONGYUAN1994/PseudotimeDE
Manuscript: [PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data](https://doi.org/10.1101/2020.11.17.387779)
### TIPS
Manuscript: [TIPS: Trajectory Inference of Pathway Significance through Pseudotime Comparison for Functional Assessment of single-cell RNAseq Data](https://doi.org/10.1101/2020.12.17.423360)
### VeloSim
Software: https://github.com/PeterZZQ/VeloSim
Manuscript: [VeloSim: Simulating single cell gene-expression and RNA velocity](https://doi.org/10.1101/2021.01.11.426277)
Abstract
The availability of high throughput single-cell RNA-Sequencing data allows researchers to study the molecular mechanisms that drive the temporal dynamics of cells during differentiation or development. Recent computational methods that build upon single-cell sequencing technology, such as trajectory inference or RNA-velocity estimation, provide a way for researchers to analyze the state of each cell during a continuous dynamic process. However, with the surge of such computational methods, there is still a lack of simulators that can model the cell temporal dynamics, and provide ground truth data to benchmark the computational methods.
Hereby we present VeloSim, a simulation software that can simulate the gene-expression kinetics in cells along continuous trajectories. VeloSim is able to take any trajectory structure composed of basic elements including “linear” and “cycle” as input, and outputs unspliced mRNA count matrix, spliced mRNA count matrix, cell pseudo-time and true RNA velocity of the cells. We demonstrate how VeloSim can be used to benchmark trajectory inference and RNA-velocity estimation methods with different amounts of biological and technical variation within the datasets. VeloSim is implemented into an R package available at https://github.com/PeterZZQ/VeloSim.
### SnapATAC
Software: https://github.com/r3fang/SnapATAC
Manuscript: [Comprehensive analysis of single cell ATAC-seq data with SnapATAC](https://doi.org/10.1038/s41467-021-21583-9)
### Spectral single cell
Software: https://github.com/mornitzan/spectral_sc
Manuscript: [Revealing lineage-related signals in single-cell gene expression using random matrix theory](https://doi.org/10.1073/pnas.1913931118)
### schubness
Software: https://github.com/EliseAld/schubness
Manuscript: [Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data](https://doi.org/10.1101/2021.03.18.435808)
### TreeVAE
Software: https://github.com/khalilouardini/treeVAE-reproducibility
Manuscript: [Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data](https://doi.org/10.1101/2021.05.28.446021)
### CoSpar
Software: https://cospar.readthedocs.io/
Manuscript: [Learning dynamics by computational integration of single cell genomic and lineage information](https://doi.org/10.1101/2021.05.06.443026)
### Lamian
Software: https://github.com/Winnie09/Lamian and https://github.com/Winnie09/trajectory_variability
Manuscript: [A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples](https://doi.org/10.1101/2021.07.10.451910)
### TedSim
Software: https://github.com/Galaxeee/TedSim
Manuscript: [TedSim: temporal dynamics simulation of single cell RNA-sequencing data and cell division history](https://doi.org/10.1101/2021.06.21.449283)
### Single-cell generalized trend model
Software: https://github.com/ElvisCuiHan/scGTM
Manuscript: [Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime](https://doi.org/10.1093/bioinformatics/btac423)
### Expression and Velocity Integration
Software: https://github.com/jranek/EVI
Manuscript: [Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction](https://doi.org/10.1101/2022.03.01.482381)
### scSTEM
Software: https://github.com/alexQiSong/scSTEM
Manuscript: [scSTEM: clustering pseudotime ordered single-cell data](https://doi.org/10.1186/s13059-022-02716-9)
### SlowMoMan
Software: https://yunwilliamyu.github.io/SlowMoMan/
Manuscript: [SlowMoMan: A web app for discovery of important features along user-drawn trajectories in 2D embeddings](https://doi.org/10.1101/2022.08.23.505019)
### Dictys
Software: https://github.com/pinellolab/dictys
Manuscript: [Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics](https://doi.org/10.1101/2022.09.14.508036)
### LEAP
Software: https://cran.r-project.org/web/packages/LEAP/index.html
Manuscript: [LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering](https://doi.org/10.1093/bioinformatics/btw729)
### TrAGEDy
Software: https://github.com/No2Ross/TrAGEDy
Manuscript: [TrAGEDy: Trajectory Alignment of Gene Expression Dynamics](https://doi.org/10.1101/2022.12.21.521424)
### Genes2Genes
Software: https://github.com/Teichlab/Genes2Genes
Manuscript: [Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation](https://doi.org/10.1101/2023.03.08.531713)
### popInfer
Software: https://github.com/maclean-lab/popInfer
Manuscript: [Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging](https://doi.org/10.1101/2023.04.18.537360)
### NeuroVelo
Manuscript: [NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data](https://doi.org/10.1101/2023.11.17.567500)
### scLANE
Software: https://github.com/jr-leary7/scLANE
Manuscript: [Interpretable trajectory inference with single-cell Linear Adaptive Negative-binomial Expression (scLANE) testing](https://doi.org/10.1101/2023.12.19.572477)
### ExDyn
Software: https://github.com/kojikoji/exdyn
Manuscript: [Inferring extrinsic factor-dependent single-cell transcriptome dynamics using a deep generative model](https://doi.org/10.1101/2024.04.01.587302)
### Hodge decomposition
Software: https://github.com/WeilabMSU/HHD
Manuscript: [Hodge Decomposition of Single-Cell RNA Velocity](https://doi.org/10.1021/acs.jcim.4c00132)
### ConsensusVelo
Manuscript: [Quantifying uncertainty in RNA velocity](https://doi.org/10.1101/2024.05.14.594102)