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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: 80
    • "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: 92
    • "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: 271
    • 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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  • scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation. scLVM was primarily designed to account for cell-cycle induced variations in single-cell RNA-seq data where cell cycle is the primary soure of variability.

    • Publications
    • "Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells"
      DOI: 10.1038/nbt.3102, Published: 2015-01-19, Citations: 1073
  • Platform: R/Python
  • Code: https://github.com/PMBio/scLVM
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  • License: Apache-2.0
  • Categories: Cell Cycle, Normalisation, Variable Genes, Visualisation
  • Added: 2016-09-08, Updated: 2016-12-08

  • scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged.

    • Publications
    • "scRecover: Discriminating True and False Zeros in Single‐Cell RNA‐Seq Data for Imputation"
      DOI: 10.1002/sim.10334, Published: 2025-02-06, Citations: 1
    • Preprints
    • "scRecover: Discriminating true and false zeros in single-cell RNA-seq data for imputation"
      DOI: 10.1101/665323, Citations: 17
  • Platform: R
  • Code: https://github.com/XuegongLab/scRecover
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  • License: GPL-1.0
  • Categories: Imputation, Normalisation
  • Added: 2019-06-20, Updated: 2025-04-13

  • 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

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