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.
Anthony Gitter. Single-cell RNA-seq pseudotime estimation algorithms. 2018. doi:10.5281/zenodo.1297422 https://github.com/agitter/single-cell-pseudotime
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, Trapnell 2015, Tanay and Regev 2017, Moon et al. 2017, Tritschler et al. 2019, Weiler et al. 2021, Ding et al. 2022, and Pan and Zhang 2023 provide a more comprehensive overview of single-cell RNA-seq and the pseudotime estimation problem. Cannoodt et al. 2016 reviews pseudotime inference algorithms. Pablo Cordero's blog post 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 and scRNA-tools. The Hemberg lab single-cell RNA-seq course has an overview of five pseudotime algorithms with usage examples. Many modern ideas for pseudotime estimation are descended from Magwene et al. 2003 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 and Todorov et al. 2018. Several of these, such as SINGE, treat pseudotime annotations as time points and extend traditional time series network inference algorithms for single-cell data. BEELINE, SERGIO, and McCalla et al. 2023 benchmark many of these specialized network inference methods.
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 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 and GitHub repository as well. Tian et al. 2018 also include trajectory inference algorithms in their single-cell RNA-seq benchmarking study. Escort is a framework to help guide the selection of a suitable trajectory inference algorithm for a dataset.
Manuscript: Reconstructing the temporal ordering of biological samples using microarray data
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
Census manuscript: Single-cell mRNA quantification and differential analysis with Census
Monocle 2 manuscript: Reversed graph embedding resolves complex single-cell trajectories
Monocle 3 manuscript: The single-cell transcriptional landscape of mammalian organogenesis
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
Cycler manuscript: Trajectories of cell-cycle progression from fixed cell populations
Wishbone software: http://www.c2b2.columbia.edu/danapeerlab/html/wishbone.html
Wishbone manuscript: Wishbone identifies bifurcating developmental trajectories from single-cell data
Software: https://github.com/gcyuan/SCUBA
Manuscript: Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
Software: https://www.biostat.wisc.edu/~kendzior/OSCOPE/
Manuscript: Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments
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
Diffusion maps manuscript (b): Diffusion maps for high-dimensional single-cell analysis of differentiation data
destiny manuscript: destiny: diffusion maps for large-scale single-cell data in R
Software: https://github.com/JohnReid/DeLorean
Manuscript: Pseudotime estimation: deconfounding single cell time series
Manuscript: Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis
Software: https://github.com/kieranrcampbell/embeddr
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
pseudogp software: https://github.com/kieranrcampbell/pseudogp
pseudogp manuscript: Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference
Analysis code: https://github.com/Teichlab/spectrum-of-differentiation-supplements
Manuscript: Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells
Software: https://github.com/jw156605/SLICER
Manuscript: SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data
Software: https://github.com/zji90/TSCAN
Manuscript: TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
Software: https://github.com/hmatsu1226/SCOUP
Software: https://github.com/mzwiessele/topslam
Manuscript: Topslam: Waddington Landscape Recovery for Single Cell Experiments
Software: https://github.com/kieranrcampbell/ouija and http://www.github.com/kieranrcampbell/ouijaflow
Manuscript: A descriptive marker gene approach to single-cell pseudotime inference
Sofware: https://bioconductor.org/packages/release/bioc/html/CellTrails.html
Manuscript: Transcriptional dynamics of hair-bundle morphogenesis revealed with CellTrails
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
Workflow manuscript: Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference
Software: https://github.com/Teichlab/GPfates
Manuscript: Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation
Software: https://github.com/dimenwarper/scimitar
Manuscript: Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories
Software: https://github.com/lengning/WaveCrest
Software: http://bioconductor.org/packages/release/bioc/html/cellTree.html
Software: http://www.github.com/kieranrcampbell/mfa
Software: https://github.com/JinmiaoChenLab/Mpath
Manuscript: Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development
Software: https://github.com/rcannood/SCORPIUS
Manuscript: SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development
Software: https://github.com/hmatsu1226/SCODE
Manuscript: SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation
Software: https://bioconductor.org/packages/release/bioc/html/switchde.html
Manuscript: switchde: inference of switch-like differential expression along single-cell trajectories
Software: https://github.com/pkathail/magic/
Manuscript: MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data
Software: https://github.com/KrishnaswamyLab/PHATE
Manuscript: Visualizing Transitions and Structure for High Dimensional Data Exploration
Manuscript: SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions
Software: https://www.andrew.cmu.edu/user/sabrinar/TASIC
Manuscript: TASIC: determining branching models from time series single cell data
Software: https://github.com/macsharma/FORKS
Manuscript: FORKS: Finding Orderings Robustly using K-means and Steiner trees
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
Software: https://github.com/tinglab/reCAT
Manuscript: Reconstructing cell cycle pseudo time-series via single-cell transcriptome data
Software: Bioconductor package and https://github.com/kieranrcampbell/phenopath
Manuscript: Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data
Software: https://github.com/ManchesterBioinference/BranchedGP
Manuscript: BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process
Software: https://github.com/cap76/BranchingGPs
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
Software: https://github.com/WangShuxiong/SoptSC
Manuscript: Assessment of clonal kinetics reveals multiple trajectories of dendritic cell development
Software: https://github.com/AllonKleinLab/PBA
Manuscript: Fundamental limits on dynamic inference from single cell snapshots
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
Software: https://github.com/roshan9128/tides
Manuscript: Learning Edge Rewiring in EMT from Single Cell Data
Software: https://pypi.org/project/wot/
Software: https://github.com/theislab/pseudodynamics
Manuscript: Inferring population dynamics from single-cell RNA-sequencing time series data
Software: https://github.com/theislab/paga
Software: https://github.com/magStra/GPseudoRank
Manuscript: GPseudoRank: MCMC for sampling from posterior distributions of pseudo-orderings using Gaussian processes
Software: https://github.com/dgrun/FateID
Manuscript: FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
Software: https://github.com/ManchesterBioinference/GrandPrix
Manuscript: GrandPrix: Scaling up the Bayesian GPLVM for single-cell data
Manuscript: Modeling acute myeloid leukemia in a continuum of differentiation states
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
Manuscript: Topographer Reveals Stochastic Dynamics of Cell Fate Decisions from Single-Cell RNA-Seq Data
Manuscript: Quantifying Waddington's epigenetic landscape: a comparison of single-cell potency measures
Software: https://github.com/ucasdp/DensityPath
Software: https://github.com/pinellolab/stream
Manuscript: STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data
Website: http://stream.pinellolab.partners.org/
Software: https://github.com/NetLand-NTU/HopLand
Software: https://github.com/jessica1338/CSHMM-for-time-series-scRNA-Seq
Manuscript: Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data
Software: https://github.com/dpeerlab/Palantir/
Manuscript: Palantir characterizes cell fate continuities in human hematopoiesis
Software: https://github.com/phoenixding/tbsp
Manuscript: Cell lineage inference from SNP and scRNA-Seq data
Software: https://github.com/architverma1/tGPLVM
Manuscript: A robust nonlinear low-dimensional manifold for single cell RNA-seq data
Software: https://github.com/bionova/sinova
Manuscript: Systematic Reconstruction of Molecular Cascades Regulating GP Development Using Single-Cell RNA-Seq
Software: Multiple repositories
Manuscript: Lineage tracing on transcriptional landscapes links state to fate during differentiation
Software: https://github.com/CABSEL/CALISTA
Manuscript: CALISTA: Clustering And Lineage Inference in Single-Cell Transcriptional Analysis
Manuscript: Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution
Software: https://github.com/wmacnair/psupertime
Manuscript: psupertime: supervised pseudotime inference for single cell RNA-seq data with sequential labels
Software: https://github.com/alexisboukouvalas/OscoNet
Manuscript: OscoNet: Inferring oscillatory gene networks
Software: https://github.com/KChen-lab/cyclum
Manuscript: Latent periodic process inference from single-cell RNA-seq data
Software: https://github.com/soedinglab/merlot
Manuscript: Reconstructing complex lineage trees from scRNA-seq data using MERLoT
Software: https://scvelo.org
Manuscript: Generalizing RNA velocity to transient cell states through dynamical modeling
Software: https://github.com/BaderLab/Tempora
Manuscript: Tempora: cell trajectory inference using time-series single-cell RNA sequencing data
Software: https://ibm.biz/cellcycletracer-aas
Manuscript: CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
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
Additional manuscript: Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport
Software: https://github.com/tinglab/redPATH
Software: https://github.com/jzthree/quasildr
Manuscript: An analytical framework for interpretable and generalizable 'quasilinear' single-cell data analysis
Software: https://github.com/akds/pseudocell
Manuscript: Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer
Software: https://github.com/Helena-todd/TinGa
Manuscript: TinGa: fast and flexible trajectory inference with Growing Neural Gas
Software: https://github.com/kimmo1019/scDEC
Manuscript: Simultaneous deep generative modeling and clustering of single cell genomic data
Software: https://github.com/wgzgithub/VeTra
Manuscript: VeTra: a new trajectory inference tool based on RNA velocity
Software: https://github.com/statway/DTFLOW
Manuscript: DTFLOW: Inference and Visualization of Single-cell Pseudo-temporal Trajectories Using Diffusion Propagation
Software: https://github.com/PeterZZQ/CellPath
Manuscript: Inference of multiple trajectories in single cell RNA-seq data from RNA velocity
Software: https://cellrank.org
CellRank manuscript: CellRank for directed single-cell fate mapping
CellRank 2 manuscript: Unified fate mapping in multiview single-cell data
Software: https://github.com/danielschw188/Revelio
Manuscript: The transcriptome dynamics of single cells during the cell cycle
Software: https://github.com/aron0093/cytopath
Manuscript: Cytopath: Simulation based inference of differentiation trajectories from RNA velocity fields
Software: https://github.com/ShobiStassen/VIA
Manuscript: VIA: Generalized and scalable trajectory inference in single-cell omics data
Software: https://github.com/zsteve/gWOT
Manuscript: Towards a mathematical theory of trajectory inference
Software: https://github.com/zsteve/StationaryOT
Manuscript: Optimal transport analysis reveals trajectories in steady-state systems
Software: https://github.com/HectorRDB/condiments
Vignettes: https://hectorrdb.github.io/condimentsPaper/
Software: https://github.com/andreariba/DeepCycle
Manuscript: Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning
Software: https://github.com/hansenlab/tricycle
Manuscript: Universal prediction of cell cycle position using transfer learning
Software: https://github.com/elolab/scshaper
Manuscript: scShaper: ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data
Software: https://github.com/LiuJJ0327/CCPE
Manuscript: CCPE: Cell Cycle Pseudotime Estimation for Single Cell RNA-seq Data
Software: https://github.com/gersteinlab/scDVF
Software: https://github.com/bauerbach95/tempo
Manuscript: Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics
Software: https://github.com/bowang-lab/DeepVelo
Manuscript: DeepVelo: Deep Learning extends RNA velocity to multi-lineage systems with cell-specific kinetics
Software: https://github.com/JuanXie19/LRT
Manuscript: LRT: T Cell Trajectory Inference by Integrative Analysis of Single Cell TCR-seq and RNA-seq data
Software: https://github.com/LiQian-XC/sctour
Manuscript: scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics
Software: https://github.com/StatBiomed/UniTVelo
Manuscript: UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference
Software: https://github.com/jaydu1/VITAE
Manuscript: Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior
Manuscript: From pseudotime to true dynamics: reconstructing a real-time axis for T cells differentiation
Software: https://github.com/KlugerLab/GeneTrajectory
Manuscript Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics
Software: https://scfates.readthedocs.io/en/latest/
Manuscript: scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single cell data
Software: https://github.com/YosefLab/velovi
Manuscript: Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
Software: https://github.com/cistrome/MIRA
Manuscript: MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells
Software: https://github.com/pinellolab/pyrovelocity
Manuscript: Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data
Software: https://github.com/elolab/Totem
Manuscript: Cell-connectivity-guided trajectory inference from single-cell data
Software: https://github.com/Starlitnightly/scltnn
Manuscript: Identify the origin and end cells and infer the trajectory of cellular fate automatically
Manuscript: Modeling Single-Cell Dynamics Using Unbalanced Parameterized Monge Maps
Software: https://github.com/kunwang34/PhyloVelo
Manuscript: Cell division history encodes directional information of fate transitions
Software: https://github.com/welch-lab/MultiVelo
Software: https://github.com/hailinphysics/sctc
Manuscript: SCTC: inference of developmental potential from single-cell transcriptional complexity
Software: https://github.com/KrishnaswamyLab/MIOFlow
Manuscript: Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Software: https://github.com/ZhangHongbo-Lab/DEAPLOG
Software: https://github.com/xznhy/rna-seq
Manuscript: GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression
Software: https://moscot-tools.org/
Manuscript: Mapping cells through time and space with moscot
Software: https://github.com/jranek/delve
Manuscript: Feature selection for preserving biological trajectories in single-cell data
Software: https://github.com/rorymaizels/velvet
Manuscript: Deep dynamical modelling of developmental trajectories with temporal transcriptomics
Software: https://github.com/theimagelab/entrain
Software: https://github.com/xiaoyeye/TFvelo
Manuscript: TFvelo: gene regulation inspired RNA velocity estimation
Software: https://github.com/BayraktarLab/cell2fate
Manuscript: Model-based inference of RNA velocity modules improves cell fate prediction
Software: https://github.com/Diebrate/population_model
Manuscript: Modeling Single Cell Trajectory Using Forward-Backward Stochastic Differential Equations
Software: https://github.com/aron0093/cy2path
Manuscript: Factorial state-space modelling for kinetic clustering and lineage inference
Software: https://github.com/yachimura-lab/scEGOT
Manuscript: scEGOT: Single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport
Manuscript: Charting cellular differentiation trajectories with Ricci flow
Manuscript: Modelling single-cell RNA-seq trajectories on a flat statistical manifold
Software: https://github.com/Noble-Lab/Sceptic
Manuscript: Pseudotime analysis for time-series single-cell sequencing and imaging data
Software: https://github.com/lamanno-epfl/velocycle/
Manuscript: Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations
Software: https://github.com/pachterlab/FGP_2024
Manuscript: Trajectory inference from single-cell genomics data with a process time model
Software: https://github.com/keita-iida/PSEUDOTIMEABC
Software: https://cran.r-project.org/package=scTEP
Manuscript: A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
Software: https://github.com/GilbertHan1011/TrajAtlas
Software: https://bitbucket.org/biocomplexity/snow/src/main/
Manuscript Variational inference of single cell time series
Software: https://github.com/lazaratan/meta-flow-matching
Manuscript: Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Software: https://github.com/rsinghlab/scMultiNODE
Manuscript: scMultiNODE: Integrative Model for Multi-Modal Temporal Single-Cell Data
Manuscript: Chromatin accessibility dynamics of myogenesis at single cell resolution
Manuscript: Comparison of computational methods for imputing single-cell RNA-sequencing data
Software: https://github.com/soedinglab/prosstt
Manuscript: PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes
Software: https://github.com/shenorrLab/cellAlign
Manuscript: Alignment of single-cell trajectories to compare cellular expression dynamics
Software: http://bioconductor.org/packages/release/bioc/html/CONFESS.html
Manuscript: CONFESS: Fluorescence-based single-cell ordering in R
Manuscript: Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes
Software: http://velocyto.org/
Manuscript: RNA velocity of single cells
Manuscript: High Resolution Comparison of Cancer-Related Developmental Processes Using Trajectory Alignment
Software: https://github.com/canzarlab/Trajan
Manuscript: Dynamic pseudo-time warping of complex single-cell trajectories
Software: https://github.com/gitter-lab/SINGE
Manuscript: Network inference with Granger causality ensembles on single-cell transcriptomics
Software: https://github.com/magStra/GPseudoClust
Manuscript: GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution
Software: http://www.bioconductor.org/packages/release/bioc/html/tradeSeq.html
Manuscript: Trajectory-based differential expression analysis for single-cell sequencing data
Software: https://github.com/YutongWangUMich/corgi
Manuscript: A gene filter for comparative analysis of single-cell RNA-sequencing trajectory datasets
Software: https://github.com/murali-group/Beeline
Manuscript: Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Software: https://github.com/aristoteleo/dynamo-release
Manuscript: Mapping Vector Field of Single Cells
Software: https://github.com/PayamDiba/SERGIO
Manuscript: A single-cell expression simulator guided by gene regulatory networks
Software: https://geneswitches.ddnetbio.com/
Manuscript: GeneSwitches : Ordering gene-expression and functional events in single-cell experiments
Software: https://github.com/ykat0/capital
Manuscript: Alignment of time-course single-cell RNA-seq data with CAPITAL
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
Software: https://bioconductor.org/packages/scHOT
Manuscript: Investigating higher-order interactions in single-cell data with scHOT
Software: https://github.com/gifford-lab/prescient
Manuscript: Generative modeling of single-cell population time series for inferring cell differentiation landscapes
Manuscript: On the Mathematics of RNA Velocity I: Theoretical Analysis
Manuscript: On the Mathematics of RNA Velocity II: Algorithmic Aspects
Software: https://github.com/SONGDONGYUAN1994/PseudotimeDE
Software: https://github.com/PeterZZQ/VeloSim
Manuscript: VeloSim: Simulating single cell gene-expression and RNA velocity
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.
Software: https://github.com/r3fang/SnapATAC
Manuscript: Comprehensive analysis of single cell ATAC-seq data with SnapATAC
Software: https://github.com/mornitzan/spectral_sc
Manuscript: Revealing lineage-related signals in single-cell gene expression using random matrix theory
Software: https://github.com/EliseAld/schubness
Manuscript: Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data
Software: https://github.com/khalilouardini/treeVAE-reproducibility
Manuscript: Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data
Software: https://cospar.readthedocs.io/
Manuscript: Learning dynamics by computational integration of single cell genomic and lineage information
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
Software: https://github.com/Galaxeee/TedSim
Manuscript: TedSim: temporal dynamics simulation of single cell RNA-sequencing data and cell division history
Software: https://github.com/ElvisCuiHan/scGTM
Software: https://github.com/jranek/EVI
Manuscript: Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Software: https://github.com/alexQiSong/scSTEM
Manuscript: scSTEM: clustering pseudotime ordered single-cell data
Software: https://yunwilliamyu.github.io/SlowMoMan/
Manuscript: SlowMoMan: A web app for discovery of important features along user-drawn trajectories in 2D embeddings
Software: https://github.com/pinellolab/dictys
Manuscript: Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics
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
Software: https://github.com/No2Ross/TrAGEDy
Manuscript: TrAGEDy: Trajectory Alignment of Gene Expression Dynamics
Software: https://github.com/Teichlab/Genes2Genes
Manuscript: Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation
Software: https://github.com/maclean-lab/popInfer
Manuscript: NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data
Software: https://github.com/jr-leary7/scLANE
Software: https://github.com/kojikoji/exdyn
Manuscript: Inferring extrinsic factor-dependent single-cell transcriptome dynamics using a deep generative model
Software: https://github.com/WeilabMSU/HHD
Manuscript: Hodge Decomposition of Single-Cell RNA Velocity
Manuscript: Quantifying uncertainty in RNA velocity
Software: https://github.com/settylab/Mellon
Manuscript: Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon
Software: https://github.com/czbiohub-sf/comparison-RNAVelo (currently private or broken)
Manuscript: Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts
Software: https://github.com/Tarun-Mahajan/noSpliceVelo
Manuscript: noSpliceVelo infers gene expression dynamics without separating unspliced and spliced transcripts
Software: https://github.com/hiuchi/LS
Manuscript: The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime
Software: https://github.com/haotian-zhuang/MUSTARD
