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A

B

  • 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: 75
    • "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: 268
    • 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

  • Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters.

    • Publications
    • "A bivariate zero-inflated negative binomial model and its applications to biomedical settings"
      DOI: 10.1177/09622802231172028, Published: 2023-05-11, Citations: 3
    • Preprints
    • "A bivariate zero-inflated negative binomial model for identifying underlying dependence with application to single cell RNA sequencing data"
      DOI: 10.1101/2020.03.06.977728, Citations: 3
  • Platform: R/C++
  • Code: https://github.com/Hunyong/BZINB
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  • License: GPL-2.0
  • Categories: Simulation
  • Added: 2020-03-18, Updated: 2023-05-12

C

D

  • DENDRO, stands for Dna based EvolutioNary tree preDiction by scRna-seq technOlogy, is an R package, which takes scRNA-seq data for a tumor (or related somatic tissues) and accurately reconstructs its phylogeny, assigning each single cell from the single cell RNA sequencing (scRNA-seq) data to a subclone.

    • Publications
    • "DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing"
      DOI: 10.1186/s13059-019-1922-x, Published: 2020-01-14, Citations: 43
    • Preprints
    • "Genetic Heterogeneity Profiling by Single Cell RNA Sequencing"
      DOI: 10.1101/457622, Citations: 1
  • Platform: R
  • Code: https://github.com/zhouzilu/DENDRO
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  • License: GPL-3.0
  • Categories: Clustering, Simulation, Variants, Visualisation
  • Added: 2018-11-14, Updated: 2020-01-22

E

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G

  • GLMsim is a single cell simulator that can simultaneously capture the library size, biology and unwanted variation and their associations via a generalized linear model, and to simulate data resembling the original experimental data in these respects

    • Preprints
    • "GLMsim: a GLM-based single cell RNA-seq simulator incorporating batch and biological effects"
      DOI: 10.1101/2024.03.20.586030, Citations: 0
  • Platform: R
  • Code: https://github.com/jiananwehi/GLMsim
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  • License: GPL-3.0
  • Categories: Simulation
  • Added: 2024-04-05, Updated: 2024-04-05

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P

  • 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: 97
    • 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

  • PROSSTT (PRObabilistic Simulations of ScRNA-seq Tree-like Topologies) is a package with code for the simulation of scRNAseq data for dynamic processes such as cell differentiation.

    • Publications
    • "PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes"
      DOI: 10.1093/bioinformatics/btz078, Published: 2019-02-01, Citations: 50
    • Preprints
    • "PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes"
      DOI: 10.1101/256941, Citations: 1
  • Platform: Python
  • Code: https://github.com/soedinglab/prosstt
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  • License: GPL-3.0
  • Categories: Simulation
  • Added: 2018-02-07, Updated: 2019-05-17

R

S

  • 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: 219
    • 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

  • An interpretable simulator that generates realistic single-cell gene expression count data with gene correlations captured

    • Publications
    • "Simulating Single-Cell Gene Expression Count Data with Preserved Gene Correlations by scDesign2"
      DOI: 10.1089/cmb.2021.0440, Published: 2022-01-01, Citations: 4
    • "scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured"
      DOI: 10.1186/s13059-021-02367-2, Published: 2021-05-25, Citations: 56
    • Preprints
    • "scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured"
      DOI: 10.1101/2020.11.17.387795, Citations: 2
  • Platform: R
  • Code: https://github.com/JSB-UCLA/scDesign2
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  • License: MIT
  • Categories: Simulation
  • Added: 2020-11-22, Updated: 2022-01-22

  • scDesign3 is an all-in-one single-cell data simulation tool by using reference datasets with different cell states(cell types, trajectories or and spatial coordinates), different modalities(gene expression, chromatin accessibility, protein abundance, methylation,etc), and complex experimental designs

    • Publications
    • "scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics"
      DOI: 10.1038/s41587-023-01772-1, Published: 2023-05-11, Citations: 54
    • Preprints
    • "A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics"
      DOI: 10.1101/2022.09.20.508796, Citations: 4
  • Platform: R
  • Code: https://github.com/SONGDONGYUAN1994/scDesign3
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  • License: MIT
  • Categories: Simulation, Visualisation
  • Added: 2022-12-16, Updated: 2023-10-27

  • Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

    • Publications
    • "Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks"
      DOI: 10.1038/s41467-019-14018-z, Published: 2020-01-09, Citations: 145
    • Preprints
    • "Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks"
      DOI: 10.1101/390153, Citations: 11
  • Platform: Python
  • Code: https://github.com/imsb-uke/scGAN
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  • License: MIT
  • Categories: Simulation
  • Added: 2019-05-17, Updated: 2019-05-17

  • Simulate single-cell RNA-SEQ data using the Splatter statistical framework but implemented in python.

    • Publications
    • "Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq"
      DOI: 10.7554/eLife.43803, Published: 2019-07-08, Citations: 353
    • Preprints
    • "Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq"
      DOI: 10.1101/310599, Citations: 8
  • Platform: Python
  • Code: https://github.com/dylkot/scsim
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  • License: MIT
  • Categories: Simulation
  • Added: 2019-07-24, Updated: 2019-07-24

T

  • 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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