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  • Embeddr creates a reduced dimensional representation of the gene space using a high-variance gene correlation graph and laplacian eigenmaps. It then fits a smooth pseudotime trajectory using principal curves.

    • Preprints
    • "Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles"
      DOI: 10.1101/027219, Citations: 24
  • Platform: R
  • Code: https://github.com/kieranrcampbell/embeddr
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  • License: GPL-3.0
  • Categories: Ordering
  • Added: 2016-09-12, Updated: 2016-09-12

  • EVI is a python package designed for evaluating multi-modal data integration strategies on combining unspliced, spliced, and RNA velocity gene expression modalities for trajectory inference and disease prediction tasks

    • Publications
    • "Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction"
      DOI: 10.1186/s13059-022-02749-0, Published: 2022-09-05, Citations: 10
    • Preprints
    • "Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction"
      DOI: 10.1101/2022.03.01.482381, Citations: 1
  • Platform: Python
  • Code: https://github.com/jranek/EVI
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  • License: MIT
  • Categories: Integration, Ordering
  • Added: 2022-03-04, Updated: 2023-01-04

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  • MIRA (Probabilistic Multimodal Models for Integrated Regulatory Analysis) is a comprehensive methodology that systematically contrasts single cell transcription and accessibility to infer the regulatory circuitry driving cells along developmental trajectories

    • Publications
    • "Multi-batch single-cell comparative atlas construction by deep learning disentanglement"
      DOI: 10.1038/s41467-023-39494-2, Published: 2023-07-12, Citations: 6
    • "MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells"
      DOI: 10.1038/s41592-022-01595-z, Published: 2022-09-06, Citations: 53
    • Preprints
    • "MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells"
      DOI: 10.1101/2021.12.06.471401, Citations: 0
  • Platform: Python
  • Code: https://github.com/cistrome/MIRA
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  • Categories: Integration, Ordering, Visualisation
  • Added: 2021-12-10, Updated: 2023-07-21

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  • The term creode was coined by C.H. Waddington, combining the Greek words for “necessary” and “path” to describe the cell state transitional trajectories that define cell fate specification. Our algorithm aims to identify consensus routes from relatively noisy single-cell data and thus we named this algorithm p- (putative) Creode.

    • Publications
    • "Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut"
      DOI: 10.1016/j.cels.2017.10.012, Published: 2018-01, Citations: 175
  • Platform: Python
  • Code: https://github.com/KenLauLab/pCreode
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  • License: GPL-2.0
  • Categories: Dimensionality Reduction, Ordering, Visualisation
  • Added: 2017-12-07, Updated: 2021-06-28

  • PhenoPath learns genomic trajectories (pseudotimes) in the presence of heterogenous environmental and genetic backgrounds encoded as additional covariates and identifies interactions between the trajectories and covariates.

    • Publications
    • "Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data"
      DOI: 10.1038/s41467-018-04696-6, Published: 2018-06-22, Citations: 99
    • Preprints
    • "Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations"
      DOI: 10.1101/159913, Citations: 1
  • Platform: R
  • Code: https://github.com/kieranrcampbell/phenopath
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  • License: Apache-2.0
  • Categories: Ordering, Simulation
  • Added: 2017-07-16, Updated: 2018-06-27

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  • scent is an R-package for analysis of single-cell RNA-Seq data. It uses single-cell entropy to help analyse and interpret such data.

    • Publications
    • "Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome"
      DOI: 10.1038/ncomms15599, Published: 2017-06-01, Citations: 268
    • "Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data"
      DOI: 10.1093/bioinformatics/btaa987, Published: 2020-11-27, Citations: 23
    • Preprints
    • "Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome"
      DOI: 10.1101/084202, Citations: 2
  • Platform: R
  • Code: https://github.com/aet21/SCENT
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  • License: GPL-3.0
  • Categories: Ordering, Stem Cells
  • Added: 2016-11-09, Updated: 2020-12-13

  • Uses probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
  • Platform: C++
  • Code: https://github.com/hmatsu1226/SCOUP
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  • License: MIT
  • Categories: Ordering
  • Added: 2016-09-08, Updated: 2016-09-08

  • scTrace is a computational method to enhance single-cell lineage data through kernelized bayesian network
  • Platform: Python
  • Code: https://github.com/czythu/scTrace
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  • License: MIT
  • Categories: Ordering
  • Added: 2024-09-15, Updated: 2024-11-20

  • Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework.

    • Publications
    • "Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq: Fig. 1."
      DOI: 10.1093/bioinformatics/btv368, Published: 2015-06-22, Citations: 52
    • Preprints
    • "Sincell: Bioconductor package for the statistical assessment of cell-state hierarchies from single-cell RNA-seq data"
      DOI: 10.1101/014472, Citations: 3
  • Platform: R
  • License: GPL-2.0-or-later
  • Categories: Clustering, Dimensionality Reduction, Ordering, Visualisation
  • Added: 2016-09-08, Updated: 2018-03-15

  • The SLICE (Single Cell Lineage Inference Using Cell Expression Similarity and Entropy) algorithm consists of two major steps: (1) measuring cell differentiation states based on the calculation of single cell entropy (scEntropy) and (2) predicting cell differentiation trajectories by ordering single cells according to their scEntropy-derived differentiation states.

    • Publications
    • "SLICE: determining cell differentiation and lineage based on single cell entropy"
      DOI: 10.1093/nar/gkw1278, Published: 2016-12-19, Citations: 64
  • Platform: R
  • Code: https://research.cchmc.org/pbge/slice.html
  • License: GPL-3.0
  • Categories: Ordering
  • Added: 2017-01-10, Updated: 2017-01-10

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  • Waddington-OT uses time-course data to infer how the probability distribution of cells in gene-expression space evolves over time, by using the mathematical approach of Optimal Transport (OT).

    • Publications
    • "Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming"
      DOI: 10.1016/j.cell.2019.01.006, Published: 2019-02, Citations: 542
    • Preprints
    • "Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming"
      DOI: 10.1101/191056, Citations: 33
  • Platform: Python/Java
  • Code: https://github.com/broadinstitute/wot
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  • License: BSD-3-Clause
  • Categories: Ordering, Visualisation
  • Added: 2019-02-11, Updated: 2019-02-11