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Tools

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A

  • adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNAseq classifiers to adversarial attacks
  • Platform: R
  • Code: https://github.com/GhislainFievet/adverSCarial
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  • License: MIT
  • Categories: Classification
  • Added: 2023-11-03, Updated: 2023-11-03

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

  • The R package BayesEATS implements the method BEATS that integrates scRNA-seq data and bulk ST data to simultaneously cluster cells, partition spatial spots into different regions, and estimate cellular enrichments of spots in the Bayesian framework

    • Publications
    • "Bayesian Joint Modeling of Single-Cell Expression Data and Bulk Spatial Transcriptomic Data"
      DOI: 10.1007/s12561-021-09308-4, Published: 2021-04-12, Citations: 1
  • Platform: R/C++
  • Code: https://github.com/jingeyu/BayesEATS
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  • License: GPL-2.0-or-later
  • Categories: Clustering, Integration
  • Added: 2021-06-11, Updated: 2021-06-11

  • A novel transfer-learning-based method for batch-effect correction in single cell RNA sequencing (scRNA-seq) data

    • Publications
    • "BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes"
      DOI: 10.1186/s13059-019-1764-6, Published: 2019-08-12, Citations: 109
    • Preprints
    • "BERMUDA: A novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes"
      DOI: 10.1101/641191, Citations: 1
  • Platform: Python/R
  • Code: https://github.com/txWang/BERMUDA
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  • License: MIT
  • Categories: Integration
  • Added: 2020-03-03, Updated: 2020-03-03

  • BLGGM is a Bayesian latent mixture Gaussian graphical model to obtain cell-type-specific gene regulatory networks from heterogeneous and zero-inflated single-cell expression data
  • Platform: R/C++
  • Code: https://github.com/WgitU/BLGGM
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  • License: GPL-2.0-or-later
  • Categories: Clustering, Gene Networks
  • Added: 2022-03-18, Updated: 2022-04-01

  • BRAPeS (BCR Reconstruction Algorithm for Paired-End Single-cell), software for reconstruction of B cell receptors (BCR) using short, paired-end single-cell RNA-sequencing.

    • Publications
    • "Reconstructing B-cell receptor sequences from short-read single-cell RNA sequencing with BRAPeS"
      DOI: 10.26508/lsa.201900371, Published: 2019-08-26, Citations: 10
    • Preprints
    • "Reconstructing B cell receptor sequences from short-read single cell RNA-sequencir with BRAPeS"
      DOI: 10.1101/389999, Citations: 0
  • Platform: Python
  • Code: https://github.com/YosefLab/BRAPeS
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  • License: Custom
  • Categories: Alignment, Immune
  • Added: 2018-08-24, Updated: 2018-08-24

  • BREMSC is an R package (with core functions jointDIMMSC and BREMSC) for joint clustering droplet-based scRNA-seq and CITE-seq data. jointDIMMSC is developed as a direct extension of DIMMSC, which assumes full indenpendency between single cell RNA and surface protein data. To take the correlation between two data sources into consideration, we further develop BREMSC, which uses random effects to incorporate the two data sources. This package can directly work on raw count data from droplet-based scRNA-seq and CITE-seq experiments without any data transformation, and it can provide clustering uncertainty for each cell.

    • Publications
    • "BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data"
      DOI: 10.1093/nar/gkaa314, Published: 2020-05-07, Citations: 67
    • Preprints
    • "BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data"
      DOI: 10.1101/2020.01.18.911461, Citations: 0
  • Platform: R
  • Code: https://github.com/tarot0410/BREMSC
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  • Categories: Clustering, Integration
  • Added: 2020-02-01, Updated: 2020-02-01

  • 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

  • CellPhoneDB is a publicly available repository of curated receptors, ligands and interactions.

    • Publications
    • "Single-cell reconstruction of the early maternal–fetal interface in humans"
      DOI: 10.1038/s41586-018-0698-6, Published: 2018-11-14, Citations: 1669
    • "CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes"
      DOI: 10.1038/s41596-020-0292-x, Published: 2020-02-26, Citations: 2234
    • Preprints
    • "Reconstructing the human first trimester fetal–maternal interface using single cell transcriptomics"
      DOI: 10.1101/429589, Citations: 5
    • "CellPhoneDB v2.0: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes"
      DOI: 10.1101/680926, Citations: 54
    • "CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data"
      arXiv: 2311.04567, Citations: 0
  • Platform: Python
  • Code: https://github.com/Teichlab/cellphonedb
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  • License: MIT
  • Categories: Gene Networks, Visualisation
  • Added: 2018-11-16, Updated: 2023-11-17

  • cellSNP aims to pileup the expressed alleles in single-cell or bulk RNA-seq data, which can be directly used for donor deconvolution in multiplexed single-cell RNA-seq data
  • Platform: Python
  • Code: https://github.com/single-cell-genetics/cellSNP
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  • License: Apache-2.0
  • Categories: Variants
  • Added: 2020-01-08, Updated: 2020-01-08

  • The cellXY package currently contains trained models to classify cells as male or female and to predict whether a cell is a male-female doublet or not
  • Platform: R
  • Code: https://github.com/phipsonlab/cellXY
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  • License: GPL-3.0
  • Categories: Classification
  • Added: 2023-02-17, Updated: 2023-02-17

  • CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification) is a consensus clustering framework that offers two methods for consensus clustering: Average Similarity (AvgSim) and Similarity Network Fusion (SNF)

    • Preprints
    • "CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification"
      DOI: 10.1101/2024.03.19.585758, Citations: 0
  • Platform: R
  • Code: https://github.com/lodimk2/chai
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  • License: MIT
  • Categories: Clustering
  • Added: 2024-04-05, Updated: 2024-04-05

  • Clustergrammer2 is an interactive WebGL heatmap Jupyter widget that is built to help researchers interactively explore single cell data (e.g. scRNA-seq). Clustergrammer2 enables unbiased hierarchical clustering, integration of prior knowledge categories, and the generation of novel signatures.
  • Platform: Python
  • Code: https://github.com/ismms-himc/clustergrammer2
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  • License: MIT
  • Categories: Classification, Clustering, Gene Sets, Interactive, Visualisation
  • Added: 2019-11-06, Updated: 2021-06-28

  • The clustermole R package provides a comprehensive meta collection of cell identity markers for thousands of human and mouse cell types sourced from a variety of databases as well as methods to query them.
  • Platform: R
  • Code: https://github.com/igordot/clustermole
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  • License: MIT
  • Categories: Classification, Marker Genes
  • Added: 2020-01-22, Updated: 2020-01-30

  • ctgGEM is an R package that combines a variety of visualization packages for single cell RNA tree hierarchies
  • Platform: R
  • Code: https://github.com/bicbioeng/ctgGEM
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  • License: GPL-2.0-or-later
  • Categories: Visualisation
  • Added: 2020-04-29, Updated: 2020-04-29

D

  • 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: 89
    • 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

  • A deep count autoencoder network to denoise scRNA-seq data and remove the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account using a deep autoencoder with zero-inflated negative binomial (ZINB) loss function.

    • Publications
    • "Single-cell RNA-seq denoising using a deep count autoencoder"
      DOI: 10.1038/s41467-018-07931-2, Published: 2019-01-23, Citations: 743
    • Preprints
    • "Single cell RNA-seq denoising using a deep count autoencoder"
      DOI: 10.1101/300681, Citations: 17
  • Platform: Python
  • Code: https://github.com/theislab/dca
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  • License: Apache-2.0
  • Categories: Imputation
  • Added: 2018-04-17, Updated: 2019-01-25

  • A deep learning-based model for gene regulatary networks (GRNs) inferrence from scRNA-seq data that transforms gene expression matrix into a correlation-based co-expression network and decouples the non-linear gene regulation patterns using graph autoencoder model (GAE)

    • Publications
    • "Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model"
      DOI: 10.1371/journal.pgen.1010942, Published: 2023-09-13, Citations: 27
  • Platform: Python
  • Code: https://github.com/JChander/DeepRIG
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  • License: Apache-2.0
  • Categories: Gene Networks
  • Added: 2023-10-20, Updated: 2023-10-20

  • A transfer learning framework to infer impressions of cellular and patient phenotypes between patients and single cells

    • Publications
    • "Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease"
      DOI: 10.1186/s13073-022-01012-2, Published: 2022-02-01, Citations: 22
    • Preprints
    • "Diagnostic Evidence GAuge of Single cells (DEGAS): A transfer learning framework to infer impressions of cellular and patient phenotypes between patients and single cells"
      DOI: 10.1101/2020.06.16.142984, Citations: 1
  • Platform: R/Python
  • Code: https://github.com/tsteelejohnson91/DEGAS
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  • License: MIT
  • Categories: Classification
  • Added: 2020-06-26, Updated: 2022-03-04

  • 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

  • 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: 68
    • 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

  • A highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning

    • Publications
    • "DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning"
      DOI: 10.1186/s13059-020-02083-3, Published: 2020-07-10, Citations: 28
  • Platform: Python
  • Code: https://github.com/xie-lab/DISC
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  • License: Apache-2.0
  • Categories: Imputation
  • Added: 2020-07-17, Updated: 2020-07-17

  • Deep Unsupervised Single-cell Clustering (DUSC) is a hybrid approach for cell type discovery in scRNA-seq data.

    • Publications
    • "A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data"
      DOI: 10.1261/rna.074427.119, Published: 2020-06-12, Citations: 14
    • Preprints
    • "A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data: Supplementary Figures and Tables"
      DOI: 10.1101/511626, Citations: 2
  • Platform: Python
  • Code: https://github.com/KorkinLab/DUSC
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  • License: Apache-2.0
  • Categories: Clustering
  • Added: 2018-01-11, Updated: 2018-01-11

E

  • Ensemble Learning for Harmonization and Annotation of Single Cells (ELeFHAnt) provides an easy to use R package for users to annotate clusters of single cells, harmonize labels across single cell datasets to generate a unified atlas and infer relationship among celltypes between two datasets

    • Preprints
    • "ELeFHAnt: A supervised machine learning approach for label harmonization and annotation of single cell RNA-seq data"
      DOI: 10.1101/2021.09.07.459342, Citations: 6
  • Platform: R
  • Code: https://github.com/praneet1988/ELeFHAnt
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  • License: GPL-3.0
  • Categories: Classification, Integration
  • Added: 2021-09-10, Updated: 2021-09-10

  • ELVAR is an R-package implementing an Extended Louvain clustering algorithm that takes cell attribute information into acccount when inferring cellular communities from scRNA-seq data

    • Publications
    • "Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data"
      DOI: 10.1038/s41467-023-39017-z, Published: 2023-06-05, Citations: 3
    • Preprints
    • "Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data"
      DOI: 10.1101/2023.04.28.538653, Citations: 1
    • "Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data"
      DOI: 10.21203/rs.3.rs-2199519/v1, Citations: 0
  • Platform: R
  • Code: https://github.com/aet21/ELVAR
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  • License: GPL-3.0
  • Categories: Clustering
  • Added: 2022-11-11, Updated: 2024-01-05

  • 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: 23
  • 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

  • EmptyNN is a novel cell-calling algorithm based on Positive-unlabeled (PU) learning which removes cell-free droplets and recovers lost cells in droplet-based single cell RNA sequencing data

    • Publications
    • "EmptyNN: A neural network based on positive and unlabeled learning to remove cell-free droplets and recover lost cells in scRNA-seq data"
      DOI: 10.1016/j.patter.2021.100311, Published: 2021-07, Citations: 12
    • Preprints
    • "EmptyNN: A neural network based on positive-unlabeled learning to remove cell-free droplets and recover lost cells in single-cell RNA sequencing data"
      DOI: 10.1101/2021.01.15.426387, Citations: 1
  • Platform: R
  • Code: https://github.com/lkmklsmn/empty_nn
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  • Categories: Quality Control
  • Added: 2021-01-22, Updated: 2021-07-27

  • EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific omics measurements from single-cell sequencing

    • Publications
    • "EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing"
      DOI: 10.1371/journal.pgen.1010251, Published: 2022-06-16, Citations: 12
    • Preprints
    • "EPIC: inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing"
      DOI: 10.1101/2021.06.09.447805, Citations: 2
  • Platform: R
  • Code: https://github.com/rujinwang/EPIC
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  • License: GPL-2.0
  • Categories: Variants, Visualisation
  • Added: 2021-06-25, Updated: 2022-06-17

  • 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: 8
    • 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

F

G

  • Useful functions to visualize single cell and spatial data.
  • Platform: R/C++
  • Code: https://github.com/YuLab-SMU/ggsc
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  • License: Artistic-2.0
  • Categories: Visualisation
  • Added: 2023-11-03, Updated: 2023-11-03

  • 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

  • GMM-Demux removes Multi-Sample-Multiplets (MSMs) in a cell hashing dataset and estimates the fraction of Same-Sample-Multiplets (SSMs) and singlets in the remaining dataset.

    • Publications
    • "GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing"
      DOI: 10.1186/s13059-020-02084-2, Published: 2020-07-30, Citations: 51
    • Preprints
    • "Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing"
      DOI: 10.1101/828483, Citations: 2
  • Platform: Python
  • Code: https://github.com/CHPGenetics/GMM-demux
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  • License: MIT
  • Categories: Quality Control
  • Added: 2019-11-08, Updated: 2020-08-06

H

  • Hopper is a mathematically motivated sketching algorithm for single-cell data, which preserves transcriptional diversity using a farthest-first traversal on the data.
  • Platform: Python
  • Code: https://github.com/bendemeo/hopper
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  • Categories: Expression Patterns
  • Added: 2019-11-12, Updated: 2019-11-12

I

  • With this package .fsa intensity files or files with sequencing lengths representing the CDR3 region of T-cell receptors can be loaded, visualized and scored for quantification of the immune repertoire.
  • Platform: R
  • Code: https://github.com/martijn-cordes/ImSpectR
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  • License: MIT
  • Categories: Quantification, Visualisation
  • Added: 2019-11-08, Updated: 2019-11-08

  • 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

  • inferCSN is an package for inferring cell-specific gene regulatory network from single-cell sequencing data
  • Platform: R/C/C++
  • Code: https://github.com/mengxu98/inferCSN
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  • License: MIT
  • Categories: Gene Networks
  • Added: 2023-10-20, Updated: 2023-10-20

  • Iscandar (Interactive Single Cell Data Analysis Report) is a set of python scripts and html/javascript files used to create interactive report for single cell rna-seq analysis.
  • Platform: Python
  • Code: https://github.com/jarny/iscandar
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  • License: MIT
  • Categories: Interactive, Visualisation
  • Added: 2017-11-20, Updated: 2017-11-20

  • Batch Effect Correction for Single-cell RNA-seq data via Iterative Supervised Mutual Nearest Neighbor Refinement

    • Publications
    • "iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement"
      DOI: 10.1093/bib/bbab122, Published: 2021-04-12, Citations: 15
    • Preprints
    • "iSMNN: Batch Effect Correction for Single-cell RNA-seq data via Iterative Supervised Mutual Nearest Neighbor Refinement"
      DOI: 10.1101/2020.11.09.375659, Citations: 3
  • Platform: R
  • Code: https://github.com/yycunc/iSMNN
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  • License: GPL-3.0
  • Categories: Integration
  • Added: 2020-11-15, Updated: 2020-11-15

J

K

  • kallisto produces transcript-level quantification estimates for RNA-seq data. It can produce transript compatibility count estimates for scRNA-seq data.

    • Publications
    • "Near-optimal probabilistic RNA-seq quantification"
      DOI: 10.1038/nbt.3519, Published: 2016-04-04, Citations: 7909
    • "Modular, efficient and constant-memory single-cell RNA-seq preprocessing"
      DOI: 10.1038/s41587-021-00870-2, Published: 2021-04-01, Citations: 326
    • "A discriminative learning approach to differential expression analysis for single-cell RNA-seq"
      DOI: 10.1038/s41592-018-0303-9, Published: 2019-01-21, Citations: 129
    • Preprints
    • "Accurate quantification of single-nucleus and single-cell RNA-seq transcripts"
      DOI: 10.1101/2022.12.02.518832, Citations: 14
    • "kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq"
      DOI: 10.1101/2023.11.21.568164, Citations: 11
    • "Identification of transcriptional signatures for cell types from single-cell RNA-Seq"
      DOI: 10.1101/258566, Citations: 10
    • "Modular and efficient pre-processing of single-cell RNA-seq"
      DOI: 10.1101/673285, Citations: 82
    • "Near-optimal RNA-Seq quantification"
      arXiv: 1505.02710, Citations: 0
  • Platform: C/C++
  • Code: https://github.com/pachterlab/kallisto
    starsN/A
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    last commitUnknown
  • License: BSD-2-Clause
  • Categories: Quantification, UMIs
  • Added: 2018-06-08, Updated: 2023-11-24

L

M

  • 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: 9
    • 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
    starsN/A
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    last commitUnknown
  • License: GPL-2.0
  • Categories: Differential Expression
  • Added: 2020-11-29, Updated: 2022-04-30

  • MarkovHC as a novel single-cell omics data analysis tool, can facilitate the stratification of cells, identification of cell population hierarchical structures, and characterization of cellular trajectories and critical points

    • Publications
    • "MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data with transition pathway and critical point detection"
      DOI: 10.1093/nar/gkab1132, Published: 2021-12-01, Citations: 9
    • Preprints
    • "MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data"
      DOI: 10.1101/2020.11.04.368043, Citations: 1
  • Platform: R
  • Code: https://github.com/ZhenyiWangTHU/MarkovHC
    starsN/A
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    last commitUnknown
  • License: GPL-3.0
  • Categories: Clustering, Visualisation
  • Added: 2021-12-10, Updated: 2021-12-10

  • 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: 47
    • 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
    starsN/A
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    last commitUnknown
  • Categories: Integration, Ordering, Visualisation
  • Added: 2021-12-10, Updated: 2023-07-21

  • MiXCR is an ultimate software platform for analysis of Next-Generation Sequencing (NGS) data for immune profiling. It supports all kinds of single cell platforms and technologies for immune profiling, including commercial vendors such as 10x Genomics or BD Rhapsody and any custom protocol (droplet-based, plate-based, combinatorial barcoding etc.). Check more at https://docs.milaboratories.com.

    • Publications
    • "Antigen receptor repertoire profiling from RNA-seq data"
      DOI: 10.1038/nbt.3979, Published: 2017-10-11, Citations: 257
    • "MiXCR: software for comprehensive adaptive immunity profiling"
      DOI: 10.1038/nmeth.3364, Published: 2015-04-29, Citations: 1375
  • Platform: Java/Kotlin
  • Code: https://github.com/milaboratory/mixcr
    starsN/A
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    last commitUnknown
  • License: Custom
  • Categories: Alignment, Allele Specific, Assembly, Clustering, Immune, UMIs
  • Added: 2022-12-25, Updated: 2022-12-25

  • Python implementation of the MNN batch correction algorithm
  • Platform: Python
  • Code: https://github.com/chriscainx/mnnpy
    starsN/A
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    last commitUnknown
  • License: BSD-3-Clause
  • Categories: Integration
  • Added: 2020-03-03, Updated: 2020-03-03

  • MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov-Arnold network and data augmentation
  • Platform: Python
  • Code: https://github.com/BioX-NKU/MultiKano
    starsN/A
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    last commitUnknown
  • License: MIT
  • Categories: Classification, Integration
  • Added: 2024-10-29, Updated: 2024-10-29

N

  • 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: 90
    • 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
    starsN/A
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    last commitUnknown
  • License: GPL-2.0
  • Categories: Differential Expression
  • Added: 2020-10-05, Updated: 2022-01-21

  • NetImpute performs identification of cell types from scRNA-seq data by interpreting multiple types of biological networks. Net library uses a static method to detect the noise data items in scRNA-seq data and develop a new imputation model for estimating real values of data nois by integrating the PPI network and gene pathways. Based on data imputed by multiple types of biological networks, an integrated approach is used to identify the cell types from scRNA-seq data.

    • Publications
    • "A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data"
      DOI: 10.1186/s12859-020-03547-w, Published: 2020-06-11, Citations: 2
  • Platform: R
  • Code: https://github.com/yiangcs001/NetImpute
    starsN/A
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    last commitUnknown
  • License: GPL-3.0
  • Categories: Imputation
  • Added: 2020-06-18, Updated: 2020-06-19

  • northstar is a Python package to identify cell types within single cell transcriptomics datasets. northstar's superpower is that it learns from cell atlases but still allows queer cells to make their own cluster if they want to.

    • Publications
    • "Northstar enables automatic classification of known and novel cell types from tumor samples"
      DOI: 10.1038/s41598-020-71805-1, Published: 2020-09-17, Citations: 13
    • Preprints
    • "northstar: leveraging cell atlases to identify healthy and neoplastic cells in transcriptomes from human tumors"
      DOI: 10.1101/820928, Citations: 3
  • Platform: Python
  • Code: https://github.com/northstaratlas/northstar
    starsN/A
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    last commitUnknown
  • License: MIT
  • Categories: Classification
  • Added: 2019-11-03, Updated: 2019-11-03

  • Necessary and Sufficient Forest (NS-Forest) for Cell Type Marker Determination from cell type clusters

    • Publications
    • "A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing"
      DOI: 10.1101/gr.275569.121, Published: 2021-06-04, Citations: 57
    • Preprints
    • "NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing"
      DOI: 10.1101/2020.09.23.308932, Citations: 8
  • Platform: Python
  • Code: https://github.com/JCVenterInstitute/NSForest
    starsN/A
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    last commitUnknown
  • License: MIT
  • Categories: Marker Genes
  • Added: 2021-11-27, Updated: 2023-02-10

O

  • Oscope: a statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq experiments.

    • Publications
    • "Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments"
      DOI: 10.1038/nmeth.3549, Published: 2015-08-24, Citations: 166
  • Platform: R
  • License: Artistic-2.0
  • Categories: Cell Cycle
  • Added: 2016-09-09, Updated: 2018-03-15

P

  • PARC, “phenotyping by accelerated refined community-partitioning” - is a fast, automated, combinatorial graph-based clustering approach that integrates hierarchical graph construction (HNSW) and data-driven graph-pruning with the new Leiden community-detection algorithm.

    • Publications
    • "PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells"
      DOI: 10.1093/bioinformatics/btaa042, Published: 2020-01-23, Citations: 89
    • Preprints
    • "PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells"
      DOI: 10.1101/765628, Citations: 3
  • Platform: Python
  • Code: https://github.com/ShobiStassen/PARC
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    last commitUnknown
  • License: MIT
  • Categories: Clustering
  • Added: 2019-09-17, Updated: 2019-09-17

  • 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: 172
  • 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: 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
    starsN/A
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    last commitUnknown
  • License: Apache-2.0
  • Categories: Ordering, Simulation
  • Added: 2017-07-16, Updated: 2018-06-27

  • R package providing functions for fitting, analyzing and visualizing single-cell RNASeq data which has been quantified by counting UMIs while accounting for different sequencing depths/detection rates between cells.
  • Platform: R
  • Code: https://github.com/tallulandrews/PoissonUMIs
    starsN/A
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    last commitUnknown
  • License: GPL-2.0
  • Categories: UMIs, Visualisation
  • Added: 2016-10-10, Updated: 2017-09-25

  • Flexible functions to calculate power, minimal sample size, or detectable minor allele frequency in both bulk tissue and single-cell eQTL analysis

    • Publications
    • "powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis"
      DOI: 10.1093/bioinformatics/btab385, Published: 2021-05-19, Citations: 21
    • Preprints
    • "powerEQTL: An R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis"
      DOI: 10.1101/2020.12.15.422954, Citations: 1
  • Platform: R
  • Code: https://github.com/sterding/powerEQTL
    starsN/A
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    last commitUnknown
  • License: GPL-2.0-or-later
  • Categories: Variants
  • Added: 2021-01-08, Updated: 2021-01-08

  • 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
    starsN/A
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    last commitUnknown
  • License: GPL-3.0
  • Categories: Simulation
  • Added: 2018-02-07, Updated: 2019-05-17

Q

R

  • Alignment, quantification and analysis of second and third generation sequencing data

    • Publications
    • "The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads"
      DOI: 10.1093/nar/gkz114, Published: 2019-02-20, Citations: 2095
    • Preprints
    • "The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads"
      DOI: 10.1101/377762, Citations: 46
  • Platform: R
  • License: GPL-3.0
  • Categories: Alignment, Quantification
  • Added: 2020-04-29, Updated: 2020-04-29

S

  • Salmon produces transcript-level quantification estimates for RNA-seq data. It includes the Alevin pipeline for quantifying droplet scRNA-seq.

    • Publications
    • "Salmon provides fast and bias-aware quantification of transcript expression"
      DOI: 10.1038/nmeth.4197, Published: 2017-03-06, Citations: 8782
    • "A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification"
      DOI: 10.1093/bioinformatics/btaa450, Published: 2020-07-13, Citations: 17
    • "Alevin efficiently estimates accurate gene abundances from dscRNA-seq data"
      DOI: 10.1186/s13059-019-1670-y, Published: 2019-03-27, Citations: 211
    • Preprints
    • "Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference"
      DOI: 10.1101/021592, Citations: 97
    • "A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification"
      DOI: 10.1101/2020.04.10.035899, Citations: 0
    • "Alevin efficiently estimates accurate gene abundances from dscRNA-seq data"
      DOI: 10.1101/335000, Citations: 2
  • Platform: C++
  • Code: https://github.com/COMBINE-lab/salmon
    starsN/A
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    last commitUnknown
  • License: GPL-3.0
  • Categories: Quantification, UMIs
  • Added: 2018-06-08, Updated: 2020-04-14

  • Sargent is a transformation- and cluster-free cell-type annotation method that operates at individual cell resolution by applying a scoring system to scRNA-seq data based on sets of marker genes associated with cell types

    • Publications
    • "A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data"
      DOI: 10.1016/j.mex.2023.102196, Published: 2023, Citations: 9
    • "A Marker Gene-Based Method for Identifying the Cell-Type of Origin from Single-Cell Rna Sequencing Data"
      DOI: 10.2139/ssrn.4359645, Published: 2023, Citations: 0
  • Platform: R
  • Categories: Classification, Marker Genes
  • Added: 2023-05-12, Updated: 2024-09-15

  • SAVER (Single-cell Analysis Via Expression Recovery) implements a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data.

    • Publications
    • "SAVER: gene expression recovery for single-cell RNA sequencing"
      DOI: 10.1038/s41592-018-0033-z, Published: 2018-06-25, Citations: 594
    • Preprints
    • "SAVER: Gene expression recovery for UMI-based single cell RNA sequencing"
      DOI: 10.1101/138677, Citations: 19
  • Platform: R
  • Code: https://github.com/mohuangx/SAVER
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    last commitUnknown
  • License: GPL-2.0
  • Categories: Imputation
  • Added: 2017-06-23, Updated: 2018-06-27

  • scCustomize is a collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R
  • Platform: R
  • Code: https://github.com/samuel-marsh/scCustomize
    starsN/A
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    last commitUnknown
  • License: GPL-3.0
  • Categories: Visualisation
  • Added: 2021-11-20, Updated: 2023-01-04

  • scDALI (single-cell differential allelic imbalance) is a statistical model and analysis framework that leverages allele-specific analyses of single-cell data to decode cell-state-specific genetic regulation

    • Publications
    • "scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation"
      DOI: 10.1186/s13059-021-02593-8, Published: 2022-01-06, Citations: 12
    • Preprints
    • "scDALI: Modelling allelic heterogeneity of DNA accessibility in single-cells reveals context-specific genetic regulation"
      DOI: 10.1101/2021.03.19.436142, Citations: 2
  • Platform: Python
  • Code: https://github.com/PMBio/scdali
    starsN/A
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    last commitUnknown
  • License: BSD-3-Clause
  • Categories: Alternative Splicing
  • Added: 2022-01-14, Updated: 2022-01-14

  • scDaPars is a bioinformatics algorithm to accurately quantify Alternative Polyadenylation (APA) events at both single-cell and single-gene resolution using standard scRNA-seq data

    • Publications
    • "Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression"
      DOI: 10.1101/gr.271346.120, Published: 2021-05-25, Citations: 37
    • Preprints
    • "Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq (scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression Analysis"
      DOI: 10.1101/2020.09.23.310649, Citations: 3
  • Platform: R
  • Code: https://github.com/YiPeng-Gao/scDaPars
    starsN/A
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    last commitUnknown
  • Categories: Alternative Splicing
  • Added: 2021-06-04, Updated: 2021-06-04

  • 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
    starsN/A
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    last commitUnknown
  • License: GPL-2.0
  • Categories: Differential Expression, Simulation
  • Added: 2016-09-08, Updated: 2018-03-14

  • scDECAF is a tool for mapping phenotype and celltype similarities in single cell RNAseq from a collection of genesets or makers such as those available from cellMarker, PanglaoDB and MSigDB
  • Platform: R
  • Code: https://github.com/DavisLaboratory/scDECAF
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    last commitUnknown
  • License: GPL-3.0
  • Categories: Classification, Gene Sets
  • Added: 2021-06-04, Updated: 2021-06-04

  • 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
    starsN/A
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    last commitUnknown
  • 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
    starsN/A
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    last commitUnknown
  • License: MIT
  • Categories: Simulation, Visualisation
  • Added: 2022-12-16, Updated: 2023-10-27

  • scDetect is a new cell type ensemble learning classification method for single-cell RNA sequencing across different data platforms, using a combination of gene expression rank-based method and majority vote ensemble machine-learning probability-based prediction method

    • Publications
    • "scDetect: a rank-based ensemble learning algorithm for cell type identification of single-cell RNA sequencing in cancer"
      DOI: 10.1093/bioinformatics/btab410, Published: 2021-05-28, Citations: 4
  • Platform: R
  • Code: https://github.com/IVDgenomicslab/scDetect
    starsN/A
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    last commitUnknown
  • License: MIT
  • Categories: Classification
  • Added: 2021-06-04, Updated: 2021-06-04

  • scDREAMER is a single-cell data integration framework that employs a novel adversarial variational autoencoder for learning lower-dimensional cellular embeddings and a batch classifier neural network for the removal of batch effects

    • Publications
    • "scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier"
      DOI: 10.1038/s41467-023-43590-8, Published: 2023-11-27, Citations: 9
    • Preprints
    • "scDREAMER: atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier"
      DOI: 10.1101/2022.07.12.499846, Citations: 0
  • Platform: Python
  • Code: https://github.com/Zafar-Lab/scDREAMER
    starsN/A
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    last commitUnknown
  • License: MIT
  • Categories: Integration
  • Added: 2022-08-12, Updated: 2024-01-05

  • 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: 254
    • "Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data"
      DOI: 10.1093/bioinformatics/btaa987, Published: 2020-11-27, Citations: 20
    • 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
    starsN/A
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    last commitUnknown
  • License: GPL-3.0
  • Categories: Ordering, Stem Cells
  • Added: 2016-11-09, Updated: 2020-12-13

  • scFEA (single cell Flux Estimation Analysis) infers single cell fluxome from single cell RNA-sequencing (scRNA-seq) data

    • Publications
    • "FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data"
      DOI: 10.1093/nar/gkad444, Published: 2023-05-22, Citations: 4
    • "A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data"
      DOI: 10.1101/gr.271205.120, Published: 2021-07-22, Citations: 99
    • Preprints
    • "A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data"
      DOI: 10.1101/2020.09.23.310656, Citations: 4
    • "scFLUX: a web server for metabolic flux and variation prediction using transcriptomics data"
      DOI: 10.1101/2022.06.18.496660, Citations: 2
  • Platform: Python
  • Code: https://github.com/changwn/scFEA
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    last commitUnknown
  • License: MIT
  • Categories: Gene Networks
  • Added: 2020-10-05, Updated: 2023-06-02

  • 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
    starsN/A
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    last commitUnknown
  • License: MIT
  • Categories: Simulation
  • Added: 2019-05-17, Updated: 2019-05-17

  • 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

  • Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space
  • Platform: R
  • Code: https://github.com/shazanfar/scHOT
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  • License: GPL-3.0
  • Categories: Expression Patterns, Visualisation
  • Added: 2020-04-29, Updated: 2020-04-29

  • 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: 1061
  • 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

  • Python version of scmap as described from the original paper of Kilesev et al. (2017) It integrates with scanpy objects.
  • Platform: Python
  • Code: https://github.com/gatocor/scmappy
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  • License: MIT
  • Categories: Classification
  • Added: 2022-06-10, Updated: 2022-06-10

  • SCMarker is a R package that performs ab initio marker selection from single cell RNA sequencing data to enhance downstream cell-type clustering, trajectory inference and cell-type specific biological analysis.

    • Publications
    • "SCMarker: Ab initio marker selection for single cell transcriptome profiling"
      DOI: 10.1371/journal.pcbi.1007445, Published: 2019-10-28, Citations: 31
    • Preprints
    • "SCMarker: ab initio marker selection for single cell transcriptome profiling"
      DOI: 10.1101/356634, Citations: 0
  • Platform: R
  • Code: https://github.com/KChen-lab/SCMarker
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  • License: GPL-2.0-or-later
  • Categories: Variable Genes
  • Added: 2018-07-18, Updated: 2018-07-18

  • scMDCF is a python package containing tools for clustering single cell multi-omics data based on cross-modality contrastive learning to learn the common latent representation and assign clustering
  • Platform: Python
  • License: MIT
  • Categories: Integration
  • Added: 2024-03-01, Updated: 2024-03-15

  • SCOIT is an implementation of a probabilistic tensor decomposition framework for single-cell multi-omics data integration
  • Platform: Python
  • Code: https://github.com/deepomicslab/SCOIT
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  • License: MIT
  • Categories: Integration
  • Added: 2022-08-12, Updated: 2022-08-12

  • SCORPION (Single-Cell Oriented Reconstruction of PANDA Individually Optimized Gene Regulatory Networks), is an R package that uses coarse-graining of single-cell/nuclei RNA-seq data to reduce sparsity and improve the ability to detect the gene regulatory network's underlying correlation structure

    • Publications
    • "Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data"
      DOI: 10.1038/s43588-024-00597-5, Published: 2024-03-04, Citations: 5
    • Preprints
    • "Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data"
      DOI: 10.1101/2023.01.20.524974, Citations: 2
  • Platform: R
  • Code: https://github.com/kuijjerlab/SCORPION
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  • License: GPL-3.0
  • Categories: Gene Networks
  • Added: 2023-01-27, Updated: 2024-05-06

  • An open source library for optimal transport methods to align single-cell datasets
  • Platform: Python
  • Code: https://github.com/scotplus/scotplus
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  • License: MIT
  • Categories: Integration
  • Added: 2024-09-15, Updated: 2024-09-15

  • 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

  • SCPA is a method for pathway analysis in single cell RNA-seq data. It’s a novel approach to pathway analysis that defines pathway activity as a change in multivariate distribution of a given pathway across conditions, rather than enrichment or over representation of genes.

    • Publications
    • "Systematic single-cell pathway analysis to characterize early T cell activation"
      DOI: 10.1016/j.celrep.2022.111697, Published: 2022-11, Citations: 50
    • Preprints
    • "Systematic Single Cell Pathway Analysis (SCPA) reveals novel pathways engaged during early T cell activation"
      DOI: 10.1101/2022.02.07.478807, Citations: 0
  • Platform: R
  • Code: https://github.com/jackbibby1/SCPA
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  • License: GPL-3.0
  • Categories: Gene Sets, Visualisation
  • Added: 2022-02-11, Updated: 2023-01-04

  • 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: 30
    • "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

  • A novel pre-processing method (scPSD) inspired by power spectral density analysis to extract important information from large-scale single-cell omics data and enhance the separation of cellular phenotypes

    • Publications
    • "Disentangling single-cell omics representation with a power spectral density-based feature extraction"
      DOI: 10.1093/nar/gkac436, Published: 2022-05-25, Citations: 5
    • Preprints
    • "Disentangling single-cell omics representation with a power spectral density-based feature extraction"
      DOI: 10.1101/2021.10.25.465657, Citations: 0
  • Platform: MATLAB/Python/R
  • Code: https://github.com/VafaeeLab/psdMAT
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  • Categories: Transformation
  • Added: 2021-10-29, Updated: 2022-06-17

  • The goal of scRAS is to measure the cell state deviation (CSD) and cell anomalousness score (CAS) which are indicators of whether one individual cell is remote from the average expression states and whether a cell is locally anomalous, respectively
  • Platform: R
  • Code: https://github.com/AristoQian/scRAS
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  • License: MIT
  • Categories: Rare Cells, Visualisation
  • Added: 2022-12-09, Updated: 2022-12-09

  • This R package aims at the implementation of a nonparametric Bayesian model named SCSC for simultaneous subject subgroup discovery and cell type detection based on the scRNA-seq data from multiple subjects

    • Publications
    • "Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data"
      DOI: 10.5705/ss.202020.0337, Published: 2023, Citations: 1
  • Platform: R/C++
  • Code: https://github.com/WgitU/SCSC
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  • License: GPL-2.0-or-later
  • Categories: Clustering
  • Added: 2021-06-11, Updated: 2021-06-11

  • 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

  • This project is an implementation of a pipeline for Single-cell RNAseq package for recovering TCR data in python.
  • Platform: Python
  • Code: https://github.com/ElementoLab/scTCRseq
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  • License: AGPL-3.0
  • Categories: Immune
  • Added: 2017-09-09, Updated: 2021-06-28

  • 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

  • This package provides methods to interactively explore and visualize datasets with hierarchies. eg. single cells datasets with hierarchy over cells at different resolutions
  • Platform: R
  • Code: https://github.com/HCBravoLab/scTreeViz
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  • License: Artistic-2.0
  • Categories: Interactive, Visualisation
  • Added: 2021-10-29, Updated: 2021-10-29

  • 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: 55
    • "A Python library for probabilistic analysis of single-cell omics data"
      DOI: 10.1038/s41587-021-01206-w, Published: 2022-02-07, Citations: 377
    • "Deep generative modeling for single-cell transcriptomics"
      DOI: 10.1038/s41592-018-0229-2, Published: 2018-11-30, Citations: 1571
    • "Joint probabilistic modeling of single-cell multi-omic data with totalVI"
      DOI: 10.1038/s41592-020-01050-x, Published: 2021-02-15, Citations: 353
    • "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: 28
    • "Interpretable factor models of single-cell RNA-seq via variational autoencoders"
      DOI: 10.1093/bioinformatics/btaa169, Published: 2020-03-16, Citations: 146
    • "Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models"
      DOI: 10.15252/msb.20209620, Published: 2021-01-25, Citations: 346
    • 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: 50
    • "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: 21
    • "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: 11
    • "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

  • Differential Abundant/Expression Analysis for Metabolomics, Proteomics and single-cell RNA sequencing Data
  • Platform: R
  • Code: https://bioconductor.org/packages/SDAMS
  • License: GPL-1.0
  • Categories: Modality
  • Added: 2021-08-06, Updated: 2021-08-06

  • SEPA provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns.
  • Platform: R
  • Code: https://github.com/zji90/SEPA
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  • License: GPL-2.0-or-later
  • Categories: Expression Patterns, Gene Sets, Interactive
  • Added: 2016-09-08, Updated: 2016-09-08

  • SEPIRA includes SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained.

    • Preprints
    • "Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data"
      DOI: 10.1101/553040, Citations: 2
  • Platform: R
  • Code: https://github.com/YC3/SEPIRA
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  • License: GPL-3.0
  • Categories: Gene Networks
  • Added: 2019-03-01, Updated: 2019-03-01

  • Provides a general framework to perform statistical inference of each gene pair and global inference of whole-scale gene pairs in gene networks using the well known Gaussian graphical model (GGM) in a time-efficient manner.

    • Publications
    • "SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks"
      DOI: 10.1371/journal.pcbi.1006369, Published: 2018-08-13, Citations: 37
  • Platform: R
  • Code: https://github.com/cran/SILGGM
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  • License: GPL-2.0-or-later
  • Categories: Gene Networks
  • Added: 2018-08-17, Updated: 2018-08-17

  • 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: 51
    • 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

  • SINCERITIES is a tool for inferring gene regulatory networks from time-stamped cross-sectional single cell transcriptional expression profiles.

    • Publications
    • "SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles"
      DOI: 10.1093/bioinformatics/btx575, Published: 2017-09-14, Citations: 169
    • Preprints
    • "SINCERITIES: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles"
      DOI: 10.1101/089110, Citations: 5
  • Platform: MATLAB/R
  • Code: https://github.com/CABSEL/SINCERITIES
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  • License: BSD-3-Clause
  • Categories: Gene Networks
  • Added: 2017-09-21, Updated: 2017-09-21

  • Sinto is a toolkit for processing aligned single-cell data
  • Platform: Python
  • Code: https://github.com/timoast/sinto
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  • License: MIT
  • Categories: UMIs
  • Added: 2020-05-22, Updated: 2020-05-22

  • 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: 61
  • Platform: R
  • Code: https://research.cchmc.org/pbge/slice.html
  • License: GPL-3.0
  • Categories: Ordering
  • Added: 2017-01-10, Updated: 2017-01-10

  • SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection

    • Publications
    • "SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection"
      DOI: 10.1093/bib/bbaa097, Published: 2020-06-26, Citations: 18
    • Preprints
    • "SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection"
      DOI: 10.1101/672261, Citations: 1
  • Platform: R
  • Code: https://github.com/yycunc/SMNN
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  • License: GPL-3.0
  • Categories: Integration
  • Added: 2019-06-20, Updated: 2020-09-10

  • SPmarker is a machine learning based approach for identification of marker genes and classification of cells in plant tissues

    • Publications
    • "Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods"
      DOI: 10.1111/nph.18053, Published: 2022-02-24, Citations: 18
    • Preprints
    • "Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods"
      DOI: 10.1101/2020.11.22.393165, Citations: 4
  • Platform: Python
  • Code: https://github.com/LiLabAtVT/SPMarker
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  • License: GPL-3.0
  • Categories: Marker Genes
  • Added: 2021-10-18, Updated: 2022-03-04

  • Performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure

    • Publications
    • "STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring"
      DOI: 10.1371/journal.pcbi.1011413, Published: 2023-08-21, Citations: 1
    • Preprints
    • "STREAK: A Supervised Cell Surface Receptor Abundance Estimation Strategy for Single Cell RNA-Sequencing Data using Feature Selection and Thresholded Gene Set Scoring"
      DOI: 10.1101/2022.11.10.516050, Citations: 0
  • Platform: R
  • Code: https://CRAN.R-project.org/package=STREAK
  • License: GPL-2.0-or-later
  • Categories: Immune
  • Added: 2022-12-09, Updated: 2024-01-05

  • Supercells is a tool which generates a single report for multiple 10x cellranger samples - thus saving time and allowing comparative QC analysis
  • Platform: Python
  • Code: https://github.com/compugen-ltd/supercells
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  • License: BSD-3-Clause
  • Categories: Quality Control
  • Added: 2021-08-06, Updated: 2021-08-06

T

  • A tool for reconstructing Transfer Entropy-based causal gene NETwork from pseudo-time ordered single cell transcriptomic data

    • Publications
    • "TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data"
      DOI: 10.1093/nar/gkaa1014, Published: 2020-11-10, Citations: 35
    • Preprints
    • "TENET: Gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data"
      DOI: 10.1101/2019.12.20.884163, Citations: 0
  • Platform: Python
  • Code: https://github.com/neocaleb/TENET
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    last commitUnknown
  • Categories: Gene Networks
  • Added: 2019-11-12, Updated: 2020-11-15

  • TRAPeS (TCR Reconstruction Algorithm for Paired-End Single-cell), software for reconstruction of T cell receptors (TCR) using short, paired-end single-cell RNA-sequencing.

    • Publications
    • "Targeted reconstruction of T cell receptor sequence from single cell RNA-seq links CDR3 length to T cell differentiation state"
      DOI: 10.1093/nar/gkx615, Published: 2017-07-17, Citations: 71
    • Preprints
    • "Targeted reconstruction of T cell receptor sequence from single cell RNA-sequencing links CDR3 length to T cell differentiation state"
      DOI: 10.1101/072744, Citations: 5
  • Platform: Python
  • Code: https://github.com/yoseflab/trapes
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  • License: Custom
  • Categories: Immune, Quantification
  • Added: 2016-11-10, Updated: 2017-09-09

  • TreeCorTreat is an open source R package that uses a tree-based correlation screen to analyze and visualize the association between phenotype and transcriptomic features and cell types at multiple cell type resolution levels

    • Preprints
    • "Tree-based Correlation Screen and Visualization for Exploring Phenotype-Cell Type Association in Multiple Sample Single-Cell RNA-Sequencing Experiments"
      DOI: 10.1101/2021.10.27.466024, Citations: 1
  • Platform: R
  • Code: https://github.com/byzhang23/TreeCorTreat
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  • License: GPL-3.0
  • Categories: Visualisation
  • Added: 2021-11-05, Updated: 2021-11-12

  • 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

U

V

  • VarTrix is a software tool for extracting single cell variant information from 10x Genomics single cell data.
  • Platform: Rust
  • Code: https://github.com/10XGenomics/vartrix
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  • License: MIT
  • Categories: Alignment, Variants
  • Added: 2018-09-14, Updated: 2018-09-14

  • This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline
  • Platform: R
  • Code: https://github.com/kstreet13/VDJdive
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  • License: Artistic-2.0
  • Categories: Immune, Integration, Visualisation
  • Added: 2022-11-04, Updated: 2022-11-04

W

  • WEDGE is a weighted low-rank matrix completion algorithm for recovering scRNA-seq gene expression data with high dropout rate.

    • Publications
    • "WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition"
      DOI: 10.1093/bib/bbab085, Published: 2021-04-08, Citations: 16
    • Preprints
    • "WEDGE: recovery of gene expression values for sparse single-cell RNA-seq datasets using matrix decomposition"
      DOI: 10.1101/864488, Citations: 1
  • Platform: MATLAB
  • Code: https://github.com/QuKunLab/WEDGE
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  • License: MIT
  • Categories: Imputation, Interactive
  • Added: 2019-12-11, Updated: 2019-12-11

  • 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: 500
    • 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

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