{"id":17141359,"url":"https://github.com/agitter/single-cell-pseudotime","last_synced_at":"2026-02-15T06:33:51.615Z","repository":{"id":45246506,"uuid":"61960760","full_name":"agitter/single-cell-pseudotime","owner":"agitter","description":"An overview of algorithms for estimating pseudotime in single-cell RNA-seq data","archived":false,"fork":false,"pushed_at":"2025-12-17T15:02:02.000Z","size":966,"stargazers_count":433,"open_issues_count":1,"forks_count":64,"subscribers_count":32,"default_branch":"master","last_synced_at":"2025-12-20T17:51:27.933Z","etag":null,"topics":["bioinformatics","pseudotime-algorithms","rna-seq","single-cell"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/agitter.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2016-06-25T20:38:57.000Z","updated_at":"2025-12-19T22:05:56.000Z","dependencies_parsed_at":"2024-04-29T03:30:01.961Z","dependency_job_id":"c3c3d440-76f3-4d2b-886f-fcbe8753ec6c","html_url":"https://github.com/agitter/single-cell-pseudotime","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/agitter/single-cell-pseudotime","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/agitter%2Fsingle-cell-pseudotime","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/agitter%2Fsingle-cell-pseudotime/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/agitter%2Fsingle-cell-pseudotime/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/agitter%2Fsingle-cell-pseudotime/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/agitter","download_url":"https://codeload.github.com/agitter/single-cell-pseudotime/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/agitter%2Fsingle-cell-pseudotime/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29471943,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-15T05:26:30.465Z","status":"ssl_error","status_checked_at":"2026-02-15T05:26:21.858Z","response_time":118,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","pseudotime-algorithms","rna-seq","single-cell"],"created_at":"2024-10-14T20:25:09.098Z","updated_at":"2026-02-15T06:33:51.602Z","avatar_url":"https://github.com/agitter.png","language":null,"funding_links":[],"categories":["Similar lists and collections"],"sub_categories":["Methods comparisons"],"readme":"# Single-cell RNA-seq pseudotime estimation algorithms\n[![Test links](https://github.com/agitter/single-cell-pseudotime/actions/workflows/links.yml/badge.svg)](https://github.com/agitter/single-cell-pseudotime/actions/workflows/links.yml)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1297422.svg)](https://doi.org/10.5281/zenodo.1297422)\n\nSingle cells, many algorithms.\nThe goal of this page is to catalog the many algorithms that estimate pseudotimes for cells based on their gene expression levels.\nThis problem is also referred to as single-cell trajectory inference or ordering.\nIt contains method names, software links, and manuscript links and simply seeks to list as many methods as possible without commentary.\nSome related methods not specifically designed for RNA-seq (e.g. mass cytometry) are included as well, as are some methods for estimating RNA velocity.\nThe list also includes methods that are specifically designed to take pseudotemporal data as input.\n\nThe initial list was created by Anthony Gitter, but pull requests are very welcome.\nThank you to the other [contributors](https://github.com/agitter/single-cell-pseudotime/graphs/contributors).\n\n\n## Citation\n\nAnthony Gitter. Single-cell RNA-seq pseudotime estimation algorithms. 2018.\ndoi:10.5281/zenodo.1297422\nhttps://github.com/agitter/single-cell-pseudotime\n\n\n## Problem overview\n\nInformally, the pseudotime estimation problem can be stated as:\n- **Given:** single-cell gene expression measurements for a heterogeneous collection of cells that is transitioning from biological state **A** to state **B**\n- **Return:** a quantitative value for each cell that represents its progress in the **A** to **B** transition\n\nThere are many ways to approach this problem, and major algorithmic steps that are common to most (but not all) methods are:\n- Reduce the dimension of the dataset\n- 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\n\nDimension reduction sometimes relies on knowledge of important marker genes and sometimes uses the full gene expression matrix.\nThe 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).\n\n[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), [Ding et al. 2022](https://doi.org/10.1038/s41576-021-00444-7), [Pan and Zhang 2023](https://pmc.ncbi.nlm.nih.gov/articles/PMC10937865/), and [Hutton and Meyer 2025](https://doi.org/10.48550/arXiv.2502.09354) provide a more comprehensive overview of single-cell RNA-seq and the pseudotime estimation problem.\n[Cannoodt et al. 2016](http://onlinelibrary.wiley.com/wol1/doi/10.1002/eji.201646347/abstract) reviews pseudotime inference algorithms.\n[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.\nFor 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/).\nThe 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.\nMany 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.\n\nSingle-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).\nSeveral 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.\n[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.\n\n## Choosing a method\n\nSome of the distinguishing factors among algorithms include:\n- Use of prior knowledge such as capture times (DeLorean) or switch-like marker genes (Ouija)\n- Modeling specific types of biological processes such as branching processes in differentiation (multiple methods) or cyclic processes (Oscope)\n- Return a single pseudotime or a posterior distribution over pseudotimes for each cell\n- Perform additional analyses after inferring pseudotimes such as regulatory network inference or identifying differentially expressed genes over pseudotime\n\n[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.\nThey benchmark both quantitative performance and assess software quality.\nSee their [website](https://dynverse.org/) and [GitHub repository](https://github.com/dynverse/dynverse) as well.\n[Tian et al. 2018](https://doi.org/10.1101/433102) also include trajectory inference algorithms in their single-cell RNA-seq benchmarking study.\n[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.\n\n## Algorithms\n\n### Temporal Reconstruction Algorithm\nManuscript: [Reconstructing the temporal ordering of biological samples using microarray data](https://doi.org/10.1093/bioinformatics/btg081)\n\n### Monocle / Monocle 2 / Monocle 3 / Census\nSoftware: https://bioconductor.org/packages/release/bioc/html/monocle.html\n\nMonocle manuscript: [The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells](https://doi.org/10.1038/nbt.2859)\n\nCensus manuscript: [Single-cell mRNA quantification and differential analysis with Census](https://doi.org/10.1038/nmeth.4150)\n\nMonocle 2 manuscript: [Reversed graph embedding resolves complex single-cell trajectories](https://doi.org/10.1038/nmeth.4402)\n\nMonocle 3 manuscript: [The single-cell transcriptional landscape of mammalian organogenesis](https://doi.org/10.1038/s41586-019-0969-x)\n\n### Wanderlust / Cycler / Wishbone\nWanderlust software: http://www.c2b2.columbia.edu/danapeerlab/html/wanderlust.html\n\nWanderlust 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)\n\nCycler manuscript: [Trajectories of cell-cycle progression from fixed cell populations](https://doi.org/10.1038/nmeth.3545)\n\nWishbone software: http://www.c2b2.columbia.edu/danapeerlab/html/wishbone.html\n\nWishbone manuscript: [Wishbone identifies bifurcating developmental trajectories from single-cell data](https://doi.org/10.1038/nbt.3569)\n\n### SCUBA\nSoftware: https://github.com/gcyuan/SCUBA\n\nManuscript: [Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape](https://doi.org/10.1073/pnas.1408993111)\n\n### Oscope\nSoftware: https://www.biostat.wisc.edu/~kendzior/OSCOPE/\n\nManuscript: [Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments](https://doi.org/10.1038/nmeth.3549)\n\n### Diffusion maps / destiny\ndestiny software: http://bioconductor.org/packages/release/bioc/html/destiny.html\n\nDiffusion maps manuscript (a): [Decoding the regulatory network of early blood development from single-cell gene expression measurements](https://doi.org/10.1038/nbt.3154)\n\nDiffusion maps manuscript (b): [Diffusion maps for high-dimensional single-cell analysis of differentiation data](https://doi.org/10.1093/bioinformatics/btv325)\n\ndestiny manuscript: [destiny: diffusion maps for large-scale single-cell data in R](https://doi.org/10.1093/bioinformatics/btv715)\n\n### DeLorean\nSoftware: https://github.com/JohnReid/DeLorean\n\nManuscript: [Pseudotime estimation: deconfounding single cell time series](https://doi.org/10.1093/bioinformatics/btw372)\n\n### Waterfall\nManuscript: [Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis](https://doi.org/10.1016/j.stem.2015.07.013)\n\n### Embeddr\nSoftware: https://github.com/kieranrcampbell/embeddr\n\nManuscript: [Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles](https://doi.org/10.1101/027219)\n\n### GP-LVM and pseudogp\nGP-LVM software: https://github.com/kieranrcampbell/gpseudotime\n\nGP-LVM manuscript: [Bayesian Gaussian Process Latent Variable Models for pseudotime inference in single-cell RNA-seq data](https://doi.org/10.1101/026872)\n\npseudogp software: https://github.com/kieranrcampbell/pseudogp\n\npseudogp manuscript: [Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference](https://doi.org/10.1371/journal.pcbi.1005212)\n\n### GP-LVM\nAnalysis code: https://github.com/Teichlab/spectrum-of-differentiation-supplements\n\nManuscript: [Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells](https://doi.org/10.1016/j.celrep.2015.12.082)\n\n### SLICER\nSoftware: https://github.com/jw156605/SLICER\n\nManuscript: [SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data](https://doi.org/10.1186/s13059-016-0975-3)\n\n### TSCAN\nSoftware: https://github.com/zji90/TSCAN\n\nManuscript: [TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis](https://doi.org/10.1093/nar/gkw430)\n\n### SCOUP\nSoftware: https://github.com/hmatsu1226/SCOUP\n\nManuscript: [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)\n\n### Topslam\nSoftware: https://github.com/mzwiessele/topslam\n\nManuscript: [Topslam: Waddington Landscape Recovery for Single Cell Experiments\n](https://doi.org/10.1101/057778)\n\n### Ouija\nSoftware: https://github.com/kieranrcampbell/ouija and http://www.github.com/kieranrcampbell/ouijaflow\n\nManuscript: [A descriptive marker gene approach to single-cell pseudotime inference](https://doi.org/10.1093/bioinformatics/bty498)\n\n### CellTrails\n\nSofware: https://bioconductor.org/packages/release/bioc/html/CellTrails.html\n\nManuscript: [Transcriptional dynamics of hair-bundle morphogenesis revealed with CellTrails](https://doi.org/10.1016/j.celrep.2018.05.002)\n\n### Slingshot\nSoftware: https://github.com/kstreet13/slingshot\n\nExtended vignette: https://github.com/drisso/bioc2016singlecell/tree/master/vignettes\n\nManuscript: [Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics](https://doi.org/10.1101/128843)\n\nWorkflow manuscript: [Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference](https://doi.org/10.12688/f1000research.12122.1)\n\n### GPfates\n\nSoftware: https://github.com/Teichlab/GPfates\n\nManuscript: [Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation](https://doi.org/10.1101/074971)\n\n### SCIMITAR\n\nSoftware: https://github.com/dimenwarper/scimitar\n\nManuscript: [Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories](https://doi.org/10.1101/070151)\n\n### WaveCrest\n\nSoftware: https://github.com/lengning/WaveCrest\n\nManuscript: [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)\n\n\n### CellTree\n\nSoftware: http://bioconductor.org/packages/release/bioc/html/cellTree.html\n\nManuscript: [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)\n\n### Bayesian hierarchical mixture of factor analysers (MFA)\n\nSoftware: http://www.github.com/kieranrcampbell/mfa\n\nManuscript: [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)\n\n### Mpath\n\nSoftware: https://github.com/JinmiaoChenLab/Mpath\n\nManuscript: [Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development](https://doi.org/10.1038/ncomms11988)\n\n### SCORPIUS\n\nSoftware: https://github.com/rcannood/SCORPIUS\n\nManuscript: [SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development](https://doi.org/10.1101/079509)\n\n### SCODE\n\nSoftware: https://github.com/hmatsu1226/SCODE\n\nManuscript: [SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation](https://doi.org/10.1093/bioinformatics/btx194)\n\n### switchde\n\nSoftware: https://bioconductor.org/packages/release/bioc/html/switchde.html\n\nManuscript: [switchde: inference of switch-like differential expression along single-cell trajectories](https://doi.org/10.1093/bioinformatics/btw798)\n\n### MAGIC\n\nSoftware: https://github.com/pkathail/magic/\n\nManuscript: [MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data](https://doi.org/10.1101/111591)\n\n### PHATE\n\nSoftware: https://github.com/KrishnaswamyLab/PHATE\n\nManuscript: [Visualizing Transitions and Structure for High Dimensional Data Exploration\n](https://doi.org/10.1101/120378)\n\n### SOMSC\n\nManuscript: [SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions](https://doi.org/10.1101/124693)\n\n### TASIC\n\nSoftware: https://www.andrew.cmu.edu/user/sabrinar/TASIC\n\nManuscript: [TASIC: determining branching models from time series single cell data](https://doi.org/10.1093/bioinformatics/btx173)\n\n### FORKS\n\nSoftware: https://github.com/macsharma/FORKS\n\nManuscript: [FORKS: Finding Orderings Robustly using K-means and Steiner trees](https://doi.org/10.1101/132811)\n\n### UNCURL\n\nSoftware: https://github.com/yjzhang/uncurl_python and https://github.com/mukhes3/UNCURL_release\n\nManuscript: [Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge\n](https://doi.org/10.1101/142398)\n\n### reCAT\n\nSoftware: https://github.com/tinglab/reCAT\n\nManuscript: [Reconstructing cell cycle pseudo time-series via single-cell transcriptome data](https://doi.org/10.1038/s41467-017-00039-z)\n\n### PhenoPath\n\nSoftware: [Bioconductor package](https://doi.org/10.18129/B9.bioc.phenopath) and https://github.com/kieranrcampbell/phenopath\n\nManuscript: [Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data](https://doi.org/10.1038/s41467-018-04696-6)\n\n### Branched Gaussian processes\n\nSoftware: https://github.com/ManchesterBioinference/BranchedGP\n\nManuscript: [BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process](https://doi.org/10.1186/s13059-018-1440-2)\n\n### Branch-recombinant Gaussian Processes\n\nSoftware: https://github.com/cap76/BranchingGPs\n\nManuscript: [Nonparametric Bayesian inference of transcriptional branching and recombination identifies regulators of early human germ cell development](https://doi.org/10.1101/167684)\n\n### MATCHER\n\nSoftware: https://github.com/jw156605/MATCHER and https://pypi.python.org/pypi/pymatcher\n\nManuscript: [MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics](https://doi.org/10.1186/s13059-017-1269-0)\n\n### SoptSC\n\nSoftware: https://github.com/WangShuxiong/SoptSC\n\nManuscript: [Low-rank Similarity Matrix Optimization Identifies Subpopulation Structure and Orders Single Cells in Pseudotime](https://doi.org/10.1101/168922)\n\n### Di-SNE\n\nManuscript: [Assessment of clonal kinetics reveals multiple trajectories of dendritic cell development](https://doi.org/10.1101/167635)\n\n### Population Balance Analysis\n\nSoftware: https://github.com/AllonKleinLab/PBA\n\nManuscript: [Fundamental limits on dynamic inference from single cell snapshots](https://doi.org/10.1101/170118)\n\n### Scanpy\n\nSoftware: https://github.com/theislab/scanpy and https://pypi.python.org/pypi/scanpy\n\nManuscript: [Scanpy for analysis of large-scale single-cell gene expression data](https://doi.org/10.1101/174029)\n\n### TIDES\n\nSoftware: https://github.com/roshan9128/tides\n\nManuscript: [Learning Edge Rewiring in EMT from Single Cell Data\n](https://doi.org/10.1101/155028)\n\n### WADDINGTON-OT\n\nSoftware: https://pypi.org/project/wot/\n\nManuscript: [Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming](https://doi.org/10.1101/191056)\n\n### pseudodynamics\n\nSoftware: https://github.com/theislab/pseudodynamics\n\nManuscript: [Inferring population dynamics from single-cell RNA-sequencing time series data](https://doi.org/10.1038/s41587-019-0088-0)\n\n### Partition-based graph abstraction\n\nSoftware: https://github.com/theislab/paga\n\nManuscript: [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)\n\n### GPseudoRank\n\nSoftware: https://github.com/magStra/GPseudoRank\n\nManuscript: [GPseudoRank: MCMC for sampling from posterior distributions of pseudo-orderings using Gaussian processes](https://doi.org/10.1093/bioinformatics/bty664)\n\n### FateID\n\nSoftware: https://github.com/dgrun/FateID\n\nManuscript: [FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data](https://doi.org/10.1101/218115)\n\n### cycleX\n\nManuscript: [cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors](https://doi.org/10.1101/222372)\n\n### GrandPrix\n\nSoftware: https://github.com/ManchesterBioinference/GrandPrix\n\nManuscript: [GrandPrix: Scaling up the Bayesian GPLVM for single-cell data](https://doi.org/10.1101/227843)\n\n### Partial differential equations\n\nManuscript: [Modeling acute myeloid leukemia in a continuum of differentiation states](https://doi.org/10.1101/237438)\n\n### scdiff\n\nSoftware: https://github.com/phoenixding/scdiff and https://pypi.python.org/pypi/scdiff/\n\nManuscript: [Reconstructing differentiation networks and their regulation from time series single cell expression data](https://doi.org/10.1101/gr.225979.117)\n\n### Topographer\n\nManuscript: [Topographer Reveals Stochastic Dynamics of Cell Fate Decisions from Single-Cell RNA-Seq Data](https://doi.org/10.1101/251207)\n\n### Markov-Chain Entropy\n\nManuscript: [Quantifying Waddington's epigenetic landscape: a comparison of single-cell potency measures](https://doi.org/10.1093/bib/bby093)\n\n### Microstates\n\nManuscript: [Machine learning methods to reverse engineer dynamic gene regulatory networks governing cell state transitions](https://doi.org/10.1101/264671)\n\n### DensityPath\n\nSoftware: https://github.com/ucasdp/DensityPath\n\nManuscript: [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)\n\n### STREAM\n\nSoftware: https://github.com/pinellolab/stream\n\nManuscript: [STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data](https://doi.org/10.1101/302554)\n\nWebsite: http://stream.pinellolab.partners.org/\n\n### HopLand\n\nSoftware: https://github.com/NetLand-NTU/HopLand\n\nManuscript: [HopLand: single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddington's epigenetic landscape](https://doi.org/10.1093/bioinformatics/btx232)\n\n### Dynamic Distribution Decomposition\n\nManuscript: [Dynamic Distribution Decomposition for Single-Cell Snapshot Time Series Identifies Subpopulations and Trajectories during iPSC Reprogramming](https://doi.org/10.1101/367789)\n\n### Continuous state HMMs \n\nSoftware: https://github.com/jessica1338/CSHMM-for-time-series-scRNA-Seq\n\nManuscript: [Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data](https://doi.org/10.1101/380568)\n\n### Palantir\n\nSoftware: https://github.com/dpeerlab/Palantir/\n\nManuscript: [Palantir characterizes cell fate continuities in human hematopoiesis](https://doi.org/10.1038/s41587-019-0068-4)\n\n### Trajectory inference Based on SNP information\n\nSoftware: https://github.com/phoenixding/tbsp\n\nManuscript: [Cell lineage inference from SNP and scRNA-Seq data](https://doi.org/10.1101/401943)\n\n### t-Distributed Gaussian Process Latent Variable Model\n\nSoftware: https://github.com/architverma1/tGPLVM\n\nManuscript: [A robust nonlinear low-dimensional manifold for single cell RNA-seq data](https://doi.org/10.1101/443044)\n\n### Sinova\n\nSoftware: https://github.com/bionova/sinova\n\nManuscript: [Systematic Reconstruction of Molecular Cascades Regulating GP Development Using Single-Cell RNA-Seq](https://doi.org/10.1016/j.celrep.2016.04.043)\n\n### Lineage tracing on transcriptional landscapes\n\nSoftware: Multiple repositories\n\nManuscript: [Lineage tracing on transcriptional landscapes links state to fate during differentiation](https://doi.org/10.1101/467886)\n\n### CALISTA\n\nSoftware: https://github.com/CABSEL/CALISTA\n\nManuscript: [CALISTA: Clustering And Lineage Inference in Single-Cell Transcriptional Analysis](https://doi.org/10.1101/257550)\n\n### TRACER\n\nManuscript: [Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution](https://doi.org/10.1101/570341)\n\n### psupertime\n\nSoftware: https://github.com/wmacnair/psupertime\n\nManuscript: [psupertime: supervised pseudotime inference for single cell RNA-seq data with sequential labels](https://doi.org/10.1101/622001)\n\n### OscoNet\n\nSoftware: https://github.com/alexisboukouvalas/OscoNet\n\nManuscript: [OscoNet: Inferring oscillatory gene networks](https://doi.org/10.1101/600049)\n\n### Cyclum\n\nSoftware: https://github.com/KChen-lab/cyclum\n\nManuscript: [Latent periodic process inference from single-cell RNA-seq data](https://doi.org/10.1101/625566)\n\n### MERLoT\n\nSoftware: https://github.com/soedinglab/merlot\n\nManuscript: [Reconstructing complex lineage trees from scRNA-seq data using MERLoT](https://doi.org/10.1093/nar/gkz706)\n\n### scVelo\n\nSoftware: https://scvelo.org\n\nManuscript: [Generalizing RNA velocity to transient cell states through dynamical modeling](https://doi.org/10.1038/s41587-020-0591-3)\n\n### Tempora\n\nSoftware: https://github.com/BaderLab/Tempora\n\nManuscript: [Tempora: cell trajectory inference using time-series single-cell RNA sequencing data](https://doi.org/10.1101/846907)\n\n### CellCycleTRACER \n\nSoftware: https://ibm.biz/cellcycletracer-aas\n\nManuscript: [CellCycleTRACER accounts for cell cycle and volume in mass cytometry data](https://doi.org/10.1038/s41467-018-03005-5)\n\n### TrajectoryNet\n\nSoftware: https://github.com/krishnaswamylab/TrajectoryNet and https://github.com/KrishnaswamyLab/Cell-Dynamics-Pipeline\n\nManuscript: [TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics](https://arxiv.org/abs/2002.04461)\n\nAdditional 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)\n\n### redPATH\n\nSoftware: https://github.com/tinglab/redPATH\n\nManuscript: [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)\n\n### GraphDR and StructDR\n\nSoftware: https://github.com/jzthree/quasildr\n\nManuscript: [An analytical framework for interpretable and generalizable 'quasilinear' single-cell data analysis](https://doi.org/10.1101/2020.04.12.022806)\n\n### Pseudocell Tracer\n\nSoftware: https://github.com/akds/pseudocell\n\nManuscript: [Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer](https://doi.org/10.1101/2020.06.26.173179)\n\n### TinGa\n\nSoftware: https://github.com/Helena-todd/TinGa\n\nManuscript: [TinGa: fast and flexible trajectory inference with Growing Neural Gas ](https://doi.org/10.1093/bioinformatics/btaa463)\n\n### scDEC\n\nSoftware: https://github.com/kimmo1019/scDEC\n\nManuscript: [Simultaneous deep generative modeling and clustering of single cell genomic data](https://doi.org/10.1101/2020.08.17.254730)\n\n### VeTra\n\nSoftware: https://github.com/wgzgithub/VeTra\n\nManuscript: [VeTra: a new trajectory inference tool based on RNA velocity](https://doi.org/10.1101/2020.09.01.277095)\n\n### DTFLOW\n\nSoftware: https://github.com/statway/DTFLOW\n\nManuscript: [DTFLOW: Inference and Visualization of Single-cell Pseudo-temporal Trajectories Using Diffusion Propagation](https://doi.org/10.1101/2020.09.10.290973)\n\n### CellPath\n\nSoftware: https://github.com/PeterZZQ/CellPath\n\nManuscript: [Inference of multiple trajectories in single cell RNA-seq data from RNA velocity](https://doi.org/10.1101/2020.09.30.321125)\n\n### CellRank / CellRank 2\n\nSoftware: https://cellrank.org\n\nCellRank manuscript: [CellRank for directed single-cell fate mapping](https://doi.org/10.1038/s41592-021-01346-6)\n\nCellRank 2 manuscript: [Unified fate mapping in multiview single-cell data](https://doi.org/10.1101/2023.07.19.549685)\n\n### Revelio\n\nSoftware: https://github.com/danielschw188/Revelio\n\nManuscript: [The transcriptome dynamics of single cells during the cell cycle](https://doi.org/10.15252/msb.20209946)\n\n### Cytopath\n\nSoftware: https://github.com/aron0093/cytopath\n\nManuscript: [Cytopath: Simulation based inference of differentiation trajectories from RNA velocity fields](https://doi.org/10.1101/2020.12.21.423801)\n\n### VIA\n\nSoftware: https://github.com/ShobiStassen/VIA\n\nManuscript: [VIA: Generalized and scalable trajectory inference in single-cell omics data](https://doi.org/10.1101/2021.02.10.430705)\n\n### Global Waddington-OT\n\nSoftware: https://github.com/zsteve/gWOT\n\nManuscript: [Towards a mathematical theory of trajectory inference](https://arxiv.org/abs/2102.09204)\n\n### StationaryOT\n\nSoftware: https://github.com/zsteve/StationaryOT\n\nManuscript: [Optimal transport analysis reveals trajectories in steady-state systems](https://doi.org/10.1101/2021.03.02.433630)\n\n### Condiments\n\nSoftware: https://github.com/HectorRDB/condiments\n\nVignettes: https://hectorrdb.github.io/condimentsPaper/\n\nManuscript: [Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression](https://doi.org/10.1101/2021.03.09.433671)\n\n### DeepCycle\n\nSoftware: https://github.com/andreariba/DeepCycle\n\nManuscript: [Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning](https://doi.org/10.1101/2021.03.17.435887)\n\n### Tricycle\n\nSoftware: https://github.com/hansenlab/tricycle\n\nManuscript: [Universal prediction of cell cycle position using transfer learning](https://doi.org/10.1101/2021.04.06.438463)\n\n### scShaper\n\nSoftware: https://github.com/elolab/scshaper\n\nManuscript: [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)\n\n### CCPE\n\nSoftware: https://github.com/LiuJJ0327/CCPE\n\nManuscript: [CCPE: Cell Cycle Pseudotime Estimation for Single Cell RNA-seq Data](https://doi.org/10.1101/2021.06.13.448263)\n\n### scDVF\n\nSoftware: https://github.com/gersteinlab/scDVF\n\nManuscript: [scDVF: Data-driven Single-cell Transcriptomic Deep Velocity Field Learning with Neural Ordinary Differential Equations](https://doi.org/10.1101/2022.02.15.480564)\n\n### Tempo\n\nSoftware: https://github.com/bauerbach95/tempo\n\nManuscript: [Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics](https://doi.org/10.1101/2022.03.15.484454)\n\n### DeepVelo\n\nSoftware: https://github.com/bowang-lab/DeepVelo\n\nManuscript: [DeepVelo: Deep Learning extends RNA velocity to multi-lineage systems with cell-specific kinetics](https://doi.org/10.1101/2022.04.03.486877)\n\n### LRT\n\nSoftware: https://github.com/JuanXie19/LRT\n\nManuscript: [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)\n\n### scTour\n\nSoftware: https://github.com/LiQian-XC/sctour\n\nManuscript: [scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics](https://doi.org/10.1101/2022.04.17.488600)\n\n### UniTVelo\n\nSoftware: https://github.com/StatBiomed/UniTVelo\n\nManuscript: [UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference](https://doi.org/10.1101/2022.04.27.489808)\n\n### VITAE\n\nSoftware: https://github.com/jaydu1/VITAE\n\nManuscript: [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)\n\n### Real-time axis for T cells\n\nManuscript: [From pseudotime to true dynamics: reconstructing a real-time axis for T cells differentiation](https://doi.org/10.1101/2022.06.09.495431)\n\n### GeneTrajectory\n\nSoftware: https://github.com/KlugerLab/GeneTrajectory\n\nManuscript [Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics](https://doi.org/10.1101/2022.07.08.499404)\n\n### scFates\n\nSoftware: https://scfates.readthedocs.io/en/latest/\n\nManuscript: [scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single cell data](https://doi.org/10.1101/2022.07.09.498657)\n\n### veloVI\n\nSoftware: https://github.com/YosefLab/velovi\n\nManuscript: [Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells](https://doi.org/10.1101/2022.08.12.503709)\n\n### MIRA\n\nSoftware: https://github.com/cistrome/MIRA\n\nManuscript: [MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells](https://doi.org/10.1038/s41592-022-01595-z)\n\n### Pyro-Velocity\n\nSoftware: https://github.com/pinellolab/pyrovelocity\n\nManuscript: [Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data](https://doi.org/10.1101/2022.09.12.507691)\n\n### Totem\n\nSoftware: https://github.com/elolab/Totem\n\nManuscript: [Cell-connectivity-guided trajectory inference from single-cell data](https://doi.org/10.1093/bioinformatics/btad515)\n\n### scLTNN\n\nSoftware: https://github.com/Starlitnightly/scltnn\n\nManuscript: [Identify the origin and end cells and infer the trajectory of cellular fate automatically](https://doi.org/10.1101/2022.09.28.510020)\n\n### UPMM\n\nManuscript: [Modeling Single-Cell Dynamics Using Unbalanced Parameterized Monge Maps](https://doi.org/10.1101/2022.10.04.510766)\n\n### PhyloVelo\n\nSoftware: https://github.com/kunwang34/PhyloVelo\n\nManuscript: [Cell division history encodes directional information of fate transitions](https://doi.org/10.1101/2022.10.06.511094)\n\n### MultiVelo\n\nSoftware: https://github.com/welch-lab/MultiVelo\n\nManuscript: [Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction](https://doi.org/10.1038/s41587-022-01476-y)\n\n### SCTC\n\nSoftware: https://github.com/hailinphysics/sctc\n\nManuscript: [SCTC: inference of developmental potential from single-cell transcriptional complexity](https://doi.org/10.1101/2022.10.14.512265)\n\n### MIOFlow\n\nSoftware: https://github.com/KrishnaswamyLab/MIOFlow\n\nManuscript: [Manifold Interpolating Optimal-Transport Flows for Trajectory Inference](https://openreview.net/forum?id=ahAEhOtVif)\n\n### DEAPLOG\n\nSoftware: https://github.com/ZhangHongbo-Lab/DEAPLOG\n\nManuscript: [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)\n\n### GCSTI\n\nSoftware: https://github.com/xznhy/rna-seq\n\nManuscript: [GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression](https://doi.org/10.1109/TCBB.2023.3266109)\n\n### moscot\n\nSoftware: https://moscot-tools.org/\n\nManuscript: [Mapping cells through time and space with moscot](https://doi.org/10.1101/2023.05.11.540374)\n\n### DELVE\n\nSoftware: https://github.com/jranek/delve\n\nManuscript: [Feature selection for preserving biological trajectories in single-cell data](https://doi.org/10.1101/2023.05.09.540043)\n\n### Velvet\n\nSoftware: https://github.com/rorymaizels/velvet\n\nManuscript: [Deep dynamical modelling of developmental trajectories with temporal transcriptomics](https://doi.org/10.1101/2023.07.06.547989)\n\n### ENTRAIN\n\nSoftware: https://github.com/theimagelab/entrain\n\nManuscript: [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)\n\n### TFvelo\n\nSoftware: https://github.com/xiaoyeye/TFvelo\n\nManuscript: [TFvelo: gene regulation inspired RNA velocity estimation](https://doi.org/10.1101/2023.07.12.548785)\n\n### TopGen\n\nManuscript: [Unraveling cell differentiation mechanisms through topological exploration of single-cell developmental trajectories](https://doi.org/10.1101/2023.07.28.551057)\n\n### cell2fate\n\nSoftware: https://github.com/BayraktarLab/cell2fate\n\nManuscript: [Model-based inference of RNA velocity modules improves cell fate prediction](https://doi.org/10.1101/2023.08.03.551650)\n\n### FBSDE\n\nSoftware: https://github.com/Diebrate/population_model\n\nManuscript: [Modeling Single Cell Trajectory Using Forward-Backward Stochastic Differential Equations](https://doi.org/10.1101/2023.08.10.552373)\n\n### cy2path\n\nSoftware: https://github.com/aron0093/cy2path\n\nManuscript: [Factorial state-space modelling for kinetic clustering and lineage inference](https://doi.org/10.1101/2023.08.21.554135)\n\n### scEGOT\n\nSoftware: https://github.com/yachimura-lab/scEGOT\n\nManuscript: [scEGOT: Single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport](https://doi.org/10.1101/2023.09.11.557102)\n\n### Ricci flow\n\nManuscript: [Charting cellular differentiation trajectories with Ricci flow](https://doi.org/10.1101/2023.07.20.549833)\n\n### Flat NB-VAE\n\nManuscript: [Modelling single-cell RNA-seq trajectories on a flat statistical manifold](https://openreview.net/pdf?id=sXRpvW3cRR)\n\n### Sceptic\n\nSoftware: https://github.com/Noble-Lab/Sceptic\n\nManuscript: [Pseudotime analysis for time-series single-cell sequencing and imaging data](https://doi.org/10.1101/2023.11.03.565575)\n\n### VeloCycle\n\nSoftware: https://github.com/lamanno-epfl/velocycle/\n\nManuscript: [Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations](https://doi.org/10.1101/2024.01.18.576093)\n\n### Chronocell\n\nSoftware: https://github.com/pachterlab/FGP_2024\n\nManuscript: [Trajectory inference from single-cell genomics data with a process time model](https://doi.org/10.1101/2024.01.26.577510)\n\n### PSEUDOTIMEABC\n\nSoftware: https://github.com/keita-iida/PSEUDOTIMEABC\n\nManuscript: [Identifying key regulatory genes in drug resistance acquisition: Modeling pseudotime trajectories of single-cell transcriptome](https://doi.org/10.1101/2024.04.25.591115)\n\n### scTEP\n\nSoftware: https://cran.r-project.org/package=scTEP\n\nManuscript: [A robust and accurate single-cell data trajectory inference method using ensemble pseudotime](https://doi.org/10.1186/s12859-023-05179-2)\n\n### TrajAtlas\n\nSoftware: https://github.com/GilbertHan1011/TrajAtlas\n\nManuscript: [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)\n\n### SNOW\n\nSoftware: https://bitbucket.org/biocomplexity/snow/src/main/\n\nManuscript [Variational inference of single cell time series](https://doi.org/10.1101/2024.08.29.610389)\n\n### Meta Flow Matching\n\nSoftware: https://github.com/lazaratan/meta-flow-matching\n\nManuscript: [Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold](https://arxiv.org/abs/2408.14608)\n\n### scMultiNODE\n\nSoftware: https://github.com/rsinghlab/scMultiNODE\n\nManuscript: [scMultiNODE: Integrative Model for Multi-Modal Temporal Single-Cell Data](https://doi.org/10.1101/2024.10.27.620531)\n\n### ARTEMIS\n\nSoftware: https://github.com/daifengwanglab/ARTEMIS\n\nManuscript: [ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data](https://doi.org/10.1101/2025.01.23.634618)\n\n### Commute time\n\nSoftware: https://github.com/houston-lab/paper-code-files/tree/main/Houston_2025\n\nManuscript: [Alternative approaches to single-cell trajectory inference using a commute time matrix](https://doi.org/10.1101/2025.02.12.635984)\n\n### Hidden-Markov Optimal Transport\n\nSoftware: https://github.com/raphael-group/HM-OT\n\nManuscript: [Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport](https://doi.org/10.1101/2025.02.14.638351)\n\n### scLTNN\n\nSoftware: https://github.com/Starlitnightly/scLTNN\n\nManuscript: [scLTNN: an innovative tool for automatically visualizing single-cell trajectories](https://doi.org/10.1093/bioadv/vbaf033)\n\n### pseudovelocity\n\nSoftware: https://gitlab.com/Xparx/pseudovelocity\n\nManuscript: [Genome-wide expression gradient estimation based on local pseudotime in single cell RNA sequencing](https://doi.org/10.1101/2025.05.01.650773)\n\n### scRDEN\n\nManuscript: [scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference](https://doi.org/10.1038/s41598-025-01969-1)\n\n### BranchSBM\n\nSoftware: https://huggingface.co/ChatterjeeLab/BranchSBM\n\nManuscript: [Branched Schrödinger Bridge Matching](https://arxiv.org/abs/2506.09007)\n \n### CellREST\n\nManuscript: [Rethinking scRNA-seq Trajectories in Phylogenetic Paradigms: Overcoming Challenges of Missing Ancestral Information](https://doi.org/10.1101/2025.07.22.664676)\n\n### Ouroboros\n\nSoftware: https://github.com/steiflab/Ouroboros\n\nManuscript: [Deep learning inference of universal dormancy pseudotime reveals the cellular targets of anti-cancer therapies](https://doi.org/10.64898/2025.12.14.692655)\n\n### GRAIL\n\nManuscript: [Generative Reconstruction of Unobserved Cellular Dynamics using Single-Cell Transcriptomic Trajectories](https://doi.org/10.64898/2025.12.29.696948)\n\n### Pseudotime Graph Diffusion\n\nManuscript: [Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectories](https://doi.org/10.64898/2026.01.27.702126)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Related topics\n\n### Cicero\n\nManuscript: [Chromatin accessibility dynamics of myogenesis at single cell resolution](https://doi.org/10.1101/155473)\n\n### Effects of imputation on cell ordering\n\nManuscript: [Comparison of computational methods for imputing single-cell RNA-sequencing data](https://doi.org/10.1101/241190)\n\n### PROSSTT\n\nSoftware: https://github.com/soedinglab/prosstt\n\nManuscript: [PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes](https://doi.org/10.1101/256941)\n\n### CellAlign\n\nSoftware: https://github.com/shenorrLab/cellAlign\n\nManuscript: [Alignment of single-cell trajectories to compare cellular expression dynamics](https://doi.org/10.1038/nmeth.4628)\n\n### CONFESS\n\nSoftware: http://bioconductor.org/packages/release/bioc/html/CONFESS.html\n\nManuscript: [CONFESS: Fluorescence-based single-cell ordering in R](https://doi.org/10.1101/407932)\n\n### Trajectory alignment\n\nSoftware: https://www.cell.com/cms/10.1016/j.cels.2018.07.006/attachment/2b57ebff-a502-4819-b8ed-8a87d17a4ae7/mmc4.zip\n\nManuscript: [Aligning Single-Cell Developmental and Reprogramming Trajectories Identifies Molecular Determinants of Myogenic Reprogramming Outcome](https://doi.org/10.1016/j.cels.2018.07.006)\n\n### ImageAEOT\n\nManuscript: [Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes](https://doi.org/10.1101/455469)\n\n### RNA velocity\n\nSoftware: http://velocyto.org/\n\nManuscript: [RNA velocity of single cells](https://doi.org/10.1038/s41586-018-0414-6)\n\n### devMap\n\nManuscript: [High Resolution Comparison of Cancer-Related Developmental Processes Using Trajectory Alignment](https://doi.org/10.1101/469601)\n\n### Trajan\n\nSoftware: https://github.com/canzarlab/Trajan\n\nManuscript: [Dynamic pseudo-time warping of complex single-cell trajectories](https://doi.org/10.1101/522672)\n\n### SINGE\n\nSoftware: https://github.com/gitter-lab/SINGE\n\nManuscript: [Network inference with Granger causality ensembles on single-cell transcriptomics](https://doi.org/10.1016/j.celrep.2022.110333)\n\n### GPseudoClust\n\nSoftware: https://github.com/magStra/GPseudoClust\n\nManuscript: [GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution](https://doi.org/10.1101/567115)\n\n### tradeSeq\n\nSoftware: http://www.bioconductor.org/packages/release/bioc/html/tradeSeq.html\n\nManuscript: [Trajectory-based differential expression analysis for single-cell sequencing data](https://doi.org/10.1038/s41467-020-14766-3)\n\n### CORGI\n\nSoftware: https://github.com/YutongWangUMich/corgi\n\nManuscript: [A gene filter for comparative analysis of single-cell RNA-sequencing trajectory datasets](https://doi.org/10.1101/637488)\n\n### BEELINE\n\nSoftware: https://github.com/murali-group/Beeline\n\nManuscript: [Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data](https://doi.org/10.1101/642926)\n\n### Dynamo\n\nSoftware: https://github.com/aristoteleo/dynamo-release\n\nManuscript: [Mapping Vector Field of Single Cells](https://doi.org/10.1101/696724)\n\n### SERGIO\n\nSoftware: https://github.com/PayamDiba/SERGIO\n\nManuscript: [A single-cell expression simulator guided by gene regulatory networks](https://doi.org/10.1101/716811)\n\n### GeneSwitches\n\nSoftware: https://geneswitches.ddnetbio.com/\n\nManuscript: [GeneSwitches : Ordering gene-expression and functional events in single-cell experiments](https://doi.org/10.1101/832626)\n\n### CAPITAL\n\nSoftware: https://github.com/ykat0/capital\n\nManuscript: [Alignment of time-course single-cell RNA-seq data with CAPITAL](https://doi.org/10.1101/859751)\n\n### Transcriptional uncertainty landscapes\n\nManuscript: [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)\n\n### Pseudo-Location\n\nManuscript: [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)\n\n### fishpond\n\nSoftware: http://bioconductor.org/packages/devel/bioc/html/fishpond.html and https://github.com/skvanburen/scUncertaintyPaperCode\n\nManuscript: [Compression of quantification uncertainty for scRNA-seq counts](https://doi.org/10.1101/2020.07.06.189639)\n\n### scHOT\n\nSoftware: https://bioconductor.org/packages/scHOT\n\nManuscript: [Investigating higher-order interactions in single-cell data with scHOT](https://doi.org/10.1038/s41592-020-0885-x)\n\n### PRESCIENT\n\nSoftware: https://github.com/gifford-lab/prescient\n\nManuscript: [Generative modeling of single-cell population time series for inferring cell differentiation landscapes](https://doi.org/10.1101/2020.08.26.269332)\n\n### Mathematics of RNA Velocity\n\nManuscript: [On the Mathematics of RNA Velocity I: Theoretical Analysis](https://doi.org/10.1101/2020.09.19.304584)\n\n### Mathematics of RNA Velocity II\n\nManuscript: [On the Mathematics of RNA Velocity II: Algorithmic Aspects](https://doi.org/10.1101/2023.06.09.544270)\n\n### PseudotimeDE\n\nSoftware: https://github.com/SONGDONGYUAN1994/PseudotimeDE\n\nManuscript: [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)\n\n### TIPS\n\nManuscript: [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)\n\n### VeloSim\n\nSoftware: https://github.com/PeterZZQ/VeloSim\n\nManuscript: [VeloSim: Simulating single cell gene-expression and RNA velocity](https://doi.org/10.1101/2021.01.11.426277)\n\n\u003cdetails\u003e\n\u003csummary\u003eAbstract\u003c/summary\u003e\nThe 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.\n\nHereby 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.\n\u003c/details\u003e\n\n### SnapATAC\n\nSoftware: https://github.com/r3fang/SnapATAC\n\nManuscript: [Comprehensive analysis of single cell ATAC-seq data with SnapATAC](https://doi.org/10.1038/s41467-021-21583-9)\n\n### Spectral single cell\n\nSoftware: https://github.com/mornitzan/spectral_sc\n\nManuscript: [Revealing lineage-related signals in single-cell gene expression using random matrix theory](https://doi.org/10.1073/pnas.1913931118)\n\n### schubness\n\nSoftware: https://github.com/EliseAld/schubness\n\nManuscript: [Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data](https://doi.org/10.1101/2021.03.18.435808)\n\n### TreeVAE\n\nSoftware: https://github.com/khalilouardini/treeVAE-reproducibility\n\nManuscript: [Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data](https://doi.org/10.1101/2021.05.28.446021)\n\n### CoSpar\n\nSoftware: https://cospar.readthedocs.io/\n\nManuscript: [Learning dynamics by computational integration of single cell genomic and lineage information](https://doi.org/10.1101/2021.05.06.443026)\n\n### Lamian\n\nSoftware: https://github.com/Winnie09/Lamian and https://github.com/Winnie09/trajectory_variability\n\nManuscript: [A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples](https://doi.org/10.1101/2021.07.10.451910)\n\n### TedSim\n\nSoftware: https://github.com/Galaxeee/TedSim\n\nManuscript: [TedSim: temporal dynamics simulation of single cell RNA-sequencing data and cell division history](https://doi.org/10.1101/2021.06.21.449283)\n\n### Single-cell generalized trend model\n\nSoftware: https://github.com/ElvisCuiHan/scGTM\n\nManuscript: [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)\n\n### Expression and Velocity Integration\n\nSoftware: https://github.com/jranek/EVI\n\nManuscript: [Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction](https://doi.org/10.1101/2022.03.01.482381)\n\n### scSTEM\n\nSoftware: https://github.com/alexQiSong/scSTEM\n\nManuscript: [scSTEM: clustering pseudotime ordered single-cell data](https://doi.org/10.1186/s13059-022-02716-9)\n\n### SlowMoMan\n\nSoftware: https://yunwilliamyu.github.io/SlowMoMan/\n\nManuscript: [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)\n\n### Dictys\n\nSoftware: https://github.com/pinellolab/dictys\n\nManuscript: [Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics](https://doi.org/10.1101/2022.09.14.508036)\n\n### LEAP\n\nSoftware: https://cran.r-project.org/web/packages/LEAP/index.html\n\nManuscript: [LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering](https://doi.org/10.1093/bioinformatics/btw729)\n\n### TrAGEDy\n\nSoftware: https://github.com/No2Ross/TrAGEDy\n\nManuscript: [TrAGEDy: Trajectory Alignment of Gene Expression Dynamics](https://doi.org/10.1101/2022.12.21.521424)\n\n### Genes2Genes\n\nSoftware: https://github.com/Teichlab/Genes2Genes\n\nManuscript: [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)\n\n### popInfer\n\nSoftware: https://github.com/maclean-lab/popInfer\n\nManuscript: [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)\n\n### NeuroVelo\n\nManuscript: [NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data](https://doi.org/10.1101/2023.11.17.567500)\n\n### scLANE\n\nSoftware: https://github.com/jr-leary7/scLANE\n\nManuscript: [Interpretable trajectory inference with single-cell Linear Adaptive Negative-binomial Expression (scLANE) testing](https://doi.org/10.1101/2023.12.19.572477)\n\n### ExDyn\n\nSoftware: https://github.com/kojikoji/exdyn\n\nManuscript: [Inferring extrinsic factor-dependent single-cell transcriptome dynamics using a deep generative model](https://doi.org/10.1101/2024.04.01.587302)\n\n### Hodge decomposition\n\nSoftware: https://github.com/WeilabMSU/HHD\n\nManuscript: [Hodge Decomposition of Single-Cell RNA Velocity](https://doi.org/10.1021/acs.jcim.4c00132)\n\n### ConsensusVelo\n\nManuscript: [Quantifying uncertainty in RNA velocity](https://doi.org/10.1101/2024.05.14.594102)\n\n### Mellon\n\nSoftware: https://github.com/settylab/Mellon\n\nManuscript: [Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon](https://doi.org/10.1038/s41592-024-02302-w)\n\n### RNA velocity benchmark\n\nSoftware: https://github.com/czbiohub-sf/comparison-RNAVelo (currently private or broken)\n\nManuscript: [Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts](https://doi.org/10.1101/2024.06.25.600667)\n\n### noSpliceVelo\n\nSoftware: https://github.com/Tarun-Mahajan/noSpliceVelo\n\nManuscript: [noSpliceVelo infers gene expression dynamics without separating unspliced and spliced transcripts](https://doi.org/10.1101/2024.08.08.607261)\n\n### Lomb-Scargle periodogram\n\nSoftware: https://github.com/hiuchi/LS\n\nManuscript: [The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime](https://doi.org/10.1101/2024.08.20.608497)\n\n### MUSTARD\n\nSoftware: https://github.com/haotian-zhuang/MUSTARD\n\nManuscript: [Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity](https://doi.org/10.1101/2024.09.14.613024)\n\n### GraphVelo\n\nSoftware: https://github.com/xing-lab-pitt/GraphVelo\n\nManuscript: [GraphVelo allows inference of multi-modal single cell velocities and molecular mechanisms](https://doi.org/10.1101/2024.12.03.626638)\n\n### mmVelo\n\nSoftware: https://github.com/nomuhyooon/mmVelo\n\nManuscript: [mmVelo: A deep generative model for estimating cell state-dependent dynamics across multiple modalities](https://doi.org/10.1101/2024.12.11.628059)\n\n### TSvelo\n\nSoftware: https://github.com/lijc0804/TSvelo\n\nManuscript: [TSvelo: Comprehensive RNA velocity inference by jointly modeling Transcription and Splicing](https://doi.org/10.1101/2024.12.24.630058)\n\n### TimeFlow\n\nSoftware: https://github.com/MargaritaLiarou1/TimeFlow\n\nManuscript: [TimeFlow: a density-driven pseudotime method for flow cytometry data analysis](https://doi.org/10.1101/2025.02.16.638508)\n\n### CGLUE-SOE\n\nManuscript: [Trajectory Inference for Multi-Omics Data Using Ordered Labels](https://doi.org/10.1101/2025.02.25.640243)\n\n### SOCS\n\nSoftware: https://github.com/algo-bio-lab/SOCS\n\nManuscript: [Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport](https://doi.org/10.1101/2025.03.19.644194)\n\n### PyEvoCell\n\nSoftware: https://github.com/Sanofi-Public/PyEvoCell\n\nManuscript: [PyEvoCell: An LLM- Augmented Single Cell Trajectory Analysis Dashboard](https://doi.org/10.1101/2024.11.21.624686)\n\n### Single-cell lipidomics\n\nSoftware: https://github.com/PaulJonasJost/Pseudo-time-trajectory-of-single-cell-lipidomics\n\nManuscript: [Pseudo-time trajectory of single-cell lipidomics: Suggestion for experimental setup and computational analysis](https://doi.org/10.1101/2025.04.11.648323)\n\n### scTransient\n\nSoftware: https://github.com/xomicsdatascience/scTransientNotebooks\n\nManuscript: [Single-Cell Trajectory Inference for Detecting Transient Events in Biological Processes](https://doi.org/10.1101/2025.05.07.652753)\n\n### Benchmarking RNA velocity\n\nManuscript: [Benchmarking RNA velocity methods across 17 independent studies](https://doi.org/10.1101/2025.08.02.668272)\n\n### BLASE\n\nSoftware: https://github.com/andrewmccluskey-uog/blase\n\nManuscript: [BLASE: Bulk Linkage Analysis for Single Cell Experiments - Teasing Out the Secrets of Bulk Transcriptomics with Trajectory Analysis](https://doi.org/10.1101/2025.09.03.673925)\n\n### scFPC-DE\n\nSoftware: https://github.com/LopezRicardo1/scFPCDE\n\nManuscript: [scFPC-DE: Robust Differential Expression Analysis Along Single Cell Trajectories via Functional Principal Component Analysis](https://doi.org/10.1101/2025.11.03.686374)\n\n### velotest\n\nSoftware: https://github.com/mackelab/velocity-hypothesis-test/\n\nManuscript: [velotest: Statistical assessment of RNA velocity embeddings reveals quality differences for reliable trajectory visualizations](https://doi.org/10.1101/2025.10.26.683064)\n\n### SEEK-VFI\n\nSoftware: https://github.com/rdanning/seekvfi\n\nManuscript: [Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI](https://doi.org/10.64898/2025.12.12.694028)\n\n### Benchmarking RNA velocity inference\n\nSoftware: https://github.com/vikkihuangkexin/VelocityBenchmarking\n\nWebsite: https://relab.xidian.edu.cn/RNAVelocity/\n\nManuscript: [Benchmarking algorithms for RNA velocity inference](https://doi.org/10.64898/2026.01.03.697314)\n\n### GRAVITY\n\nSoftware: https://github.com/CSUBioGroup/GRAVITY\n\nManuscript: [GRAVITY: Dynamic gene regulatory network-enhanced RNA velocity modeling for trajectory inference and biological discovery](https://doi.org/10.64898/2026.01.31.702983)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagitter%2Fsingle-cell-pseudotime","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fagitter%2Fsingle-cell-pseudotime","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagitter%2Fsingle-cell-pseudotime/lists"}