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  • DIALOGUE is a dimensionality reduction algorithm that uses cross-cell-type associations to identify multicellular programs and map the cell transcriptome as a function of its environment.Given single-cell data, it combines penalized matrix decomposition with multilevel modeling to identify generalizable MCPs and examines their association with specific phenotypes of inter.

    • Publications
    • "DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data"
      DOI: 10.1038/s41587-022-01288-0, Published: 2022-05-05, Citations: 75
    • Preprints
    • "Mapping multicellular programs from single-cell profiles"
      DOI: 10.1101/2020.08.11.245472, Citations: 6
  • Platform: R
  • Code: https://github.com/livnatje/DIALOGUE
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  • Categories: Dimensionality Reduction
  • Added: 2020-09-08, Updated: 2022-05-20

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  • Infercnv is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired by InferCNV, but plays nicely with scanpy and is much more scalable.
  • Platform: Python
  • Code: https://github.com/icbi-lab/infercnvpy
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  • License: BSD-3-Clause
  • Categories: Clustering, Dimensionality Reduction, Variants
  • Added: 2021-02-12, Updated: 2021-02-12

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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: 174
  • 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

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  • scGraphNE is a graph autoencoder network where the encoder based on multi-layer graph convolutional networks extracts high-order representations of cells and genes from the cell-gene bipartite graph
  • Platform: Python
  • Code: https://github.com/sldyns/scGraphNE
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  • License: MIT
  • Categories: Dimensionality Reduction
  • Added: 2023-07-28, Updated: 2023-07-28

  • The scPCA package implements sparse constrastive PCA, to accomplish both of these tasks in the context of high-dimensional biological data. In addition to implementing this newly developed technique, the scPCA package also implements cPCA and generalizations thereof.

    • Publications
    • "Exploring high-dimensional biological data with sparse contrastive principal component analysis"
      DOI: 10.1093/bioinformatics/btaa176, Published: 2020-03-16, Citations: 34
    • "scPCA: A toolbox for sparse contrastive principal component analysis in R"
      DOI: 10.21105/joss.02079, Published: 2020-02-25, Citations: 0
    • Preprints
    • "Exploring High-Dimensional Biological Data with Sparse Contrastive Principal Component Analysis"
      DOI: 10.1101/836650, Citations: 1
  • Platform: R
  • Code: https://github.com/PhilBoileau/scPCA
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  • License: MIT
  • Categories: Dimensionality Reduction
  • Added: 2019-11-12, Updated: 2020-03-12

  • scValue is a Python package designed for efficient value-based subsampling of large scRNA-seq datasets
  • Platform: Python
  • Code: https://github.com/LHBCB/scvalue
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  • License: BSD-3-Clause
  • Categories: Dimensionality Reduction
  • Added: 2024-11-20, Updated: 2024-11-20

  • scvi-tools (single-cell variational inference tools) is a package for probabilistic modeling of single-cell omics data, built on top of PyTorch and Anndata

    • Publications
    • "PeakVI: A deep generative model for single-cell chromatin accessibility analysis"
      DOI: 10.1016/j.crmeth.2022.100182, Published: 2022-03, Citations: 61
    • "A Python library for probabilistic analysis of single-cell omics data"
      DOI: 10.1038/s41587-021-01206-w, Published: 2022-02-07, Citations: 425
    • "Deep generative modeling for single-cell transcriptomics"
      DOI: 10.1038/s41592-018-0229-2, Published: 2018-11-30, Citations: 1654
    • "Joint probabilistic modeling of single-cell multi-omic data with totalVI"
      DOI: 10.1038/s41592-020-01050-x, Published: 2021-02-15, Citations: 373
    • "An empirical Bayes method for differential expression analysis of single cells with deep generative models"
      DOI: 10.1073/pnas.2209124120, Published: 2023-05-16, Citations: 31
    • "Interpretable factor models of single-cell RNA-seq via variational autoencoders"
      DOI: 10.1093/bioinformatics/btaa169, Published: 2020-03-16, Citations: 153
    • "Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models"
      DOI: 10.15252/msb.20209620, Published: 2021-01-25, Citations: 380
    • Preprints
    • "Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells"
      DOI: 10.1101/2020.05.08.083337, Citations: 8
    • "scvi-tools: a library for deep probabilistic analysis of single-cell omics data"
      DOI: 10.1101/2021.04.28.441833, Citations: 55
    • "MultiVI: deep generative model for the integration of multi-modal data"
      DOI: 10.1101/2021.08.20.457057, Citations: 51
    • "An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models"
      DOI: 10.1101/2022.05.27.493625, Citations: 5
    • "Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing"
      DOI: 10.1101/292037, Citations: 22
    • "Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models"
      DOI: 10.1101/532895, Citations: 20
    • "Interpretable factor models of single-cell RNA-seq via variational autoencoders"
      DOI: 10.1101/737601, Citations: 8
    • "Deep Generative Models for Detecting Differential Expression in Single Cells"
      DOI: 10.1101/794289, Citations: 13
    • "Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data"
      DOI: 10.1101/794875, Citations: 12
    • "A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements"
      arXiv: 1905.02269, Citations: 0
  • Platform: Python
  • Code: https://github.com/YosefLab/scvi-tools
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  • License: BSD-3-Clause
  • Categories: Classification, Differential Expression, Dimensionality Reduction, Imputation, Integration, Normalisation, Quality Control
  • Added: 2018-04-04, Updated: 2024-01-05

  • 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

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