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  • Bayesian Analysis of single-cell RNA-seq data. Estimates cell-specific normalization constants. Technical variability is quantified based on spike-in genes. The total variability of the expression counts is decomposed into technical and biological components.

    • Publications
    • "Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data"
      DOI: 10.1016/j.cels.2018.06.011, Published: 2018-09, Citations: 82
    • "Beyond comparisons of means: understanding changes in gene expression at the single-cell level"
      DOI: 10.1186/s13059-016-0930-3, Published: 2016-04-15, Citations: 93
    • "BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data"
      DOI: 10.12688/f1000research.74416.1, Published: 2022-01-18, Citations: 0
    • "BASiCS: Bayesian Analysis of Single-Cell Sequencing Data"
      DOI: 10.1371/journal.pcbi.1004333, Published: 2015-06-24, Citations: 275
    • Preprints
    • "Beyond comparisons of means: understanding changes in gene expression at the single-cell level"
      DOI: 10.1101/035949, Citations: 2
    • "Robust expression variability testing reveals heterogeneous T cell responses"
      DOI: 10.1101/237214, Citations: 1
  • Platform: R
  • Code: https://github.com/catavallejos/BASiCS
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  • License: GPL-2.0-or-later
  • Categories: Differential Expression, Normalisation, Simulation, Variable Genes
  • Added: 2016-09-08, Updated: 2022-02-04

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  • D3E is a tool for identifying differentially-expressed genes, based on single-cell RNA-seq data. D3E consists of two modules: one for identifying differentially expressed (DE) genes, and one for fitting the parameters of a Poisson-Beta distribution.

    • Publications
    • "Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data"
      DOI: 10.1186/s12859-016-0944-6, Published: 2016-02-29, Citations: 90
    • Preprints
    • "Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data"
      DOI: 10.1101/020735, Citations: 2
  • Platform: Python
  • Code: https://github.com/hemberg-lab/D3E
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  • License: GPL-3.0
  • Categories: Differential Expression
  • Added: 2016-09-09, Updated: 2016-09-09

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  • MarcoPolo is a clustering-free approach to the exploration of differentially expressed genes along with group information in single-cell RNA-seq data

    • Publications
    • "MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering"
      DOI: 10.1093/nar/gkac216, Published: 2022-04-14, Citations: 11
    • Preprints
    • "MarcoPolo: a clustering-free approach to the exploration of differentially expressed genes along with group information in single-cell RNA-seq data"
      DOI: 10.1101/2020.11.23.393900, Citations: 1
  • Platform: Python
  • Code: https://github.com/chanwkimlab/MarcoPolo
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  • License: GPL-2.0
  • Categories: Differential Expression
  • Added: 2020-11-29, Updated: 2022-04-30

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  • NEBULA provides fast algorithms for fitting negative binomial and Poisson mixed models for analyzing large-scale multi-subject single-cell data

    • Publications
    • "NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data"
      DOI: 10.1038/s42003-021-02146-6, Published: 2021-05-26, Citations: 107
    • Preprints
    • "NEBULA: a fast negative binomial mixed model for differential expression and co-expression analyses of large-scale multi-subject single-cell data"
      DOI: 10.1101/2020.09.24.311662, Citations: 5
  • Platform: R/C++
  • Code: https://github.com/lhe17/nebula
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  • License: GPL-2.0
  • Categories: Differential Expression
  • Added: 2020-10-05, Updated: 2022-01-21

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  • scDD (Single-Cell Differential Distributions) is a framework to identify genes with different expression patterns between biological groups of interest. In addition to traditional differential expression, it can detect differences that are more complex and subtle than a mean shift.

    • Publications
    • "A statistical approach for identifying differential distributions in single-cell RNA-seq experiments"
      DOI: 10.1186/s13059-016-1077-y, Published: 2016-10-25, Citations: 225
    • Preprints
    • "scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments"
      DOI: 10.1101/035501, Citations: 5
  • Platform: R
  • Code: https://github.com/kdkorthauer/scDD
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  • License: GPL-2.0
  • Categories: Differential Expression, Simulation
  • Added: 2016-09-08, Updated: 2018-03-14

  • 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: 64
    • "A Python library for probabilistic analysis of single-cell omics data"
      DOI: 10.1038/s41587-021-01206-w, Published: 2022-02-07, Citations: 473
    • "Deep generative modeling for single-cell transcriptomics"
      DOI: 10.1038/s41592-018-0229-2, Published: 2018-11-30, Citations: 1742
    • "Joint probabilistic modeling of single-cell multi-omic data with totalVI"
      DOI: 10.1038/s41592-020-01050-x, Published: 2021-02-15, Citations: 397
    • "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: 156
    • "Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models"
      DOI: 10.15252/msb.20209620, Published: 2021-01-25, Citations: 422
    • 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: 59
    • "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: 9
    • "Deep Generative Models for Detecting Differential Expression in Single Cells"
      DOI: 10.1101/794289, Citations: 15
    • "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

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  • R package for differential expression (DE) analysis and gene set testing (GST) in single-cell RNA-seq (scRNA-seq) data

    • Publications
    • "TWO‐SIGMA: A novel two‐component single cell model‐based association method for single‐cell RNA‐seq data"
      DOI: 10.1002/gepi.22361, Published: 2020-09-29, Citations: 10
    • "TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell–cell correlation"
      DOI: 10.1093/bib/bbac084, Published: 2022-03-24, Citations: 5
    • Preprints
    • "TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation"
      DOI: 10.1101/2021.01.24.427979, Citations: 1
    • "TWO-SIGMA: a novel TWO-component SInGle cell Model-based Association method for single-cell RNA-seq data"
      DOI: 10.1101/709238, Citations: 1
  • Platform: R
  • Code: https://github.com/edvanburen/twosigma
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  • License: GPL-2.0
  • Categories: Differential Expression, Gene Sets, Simulation
  • Added: 2021-01-29, Updated: 2022-04-30

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