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  • 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

  • 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

  • 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

C

  • 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

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

  • 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

  • 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

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

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  • 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
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  • License: GPL-3.0
  • Categories: Clustering, Visualisation
  • Added: 2021-12-10, Updated: 2021-12-10

  • 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
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  • License: Custom
  • Categories: Alignment, Allele Specific, Assembly, Clustering, Immune, UMIs
  • Added: 2022-12-25, Updated: 2022-12-25

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  • 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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  • License: MIT
  • Categories: Clustering
  • Added: 2019-09-17, Updated: 2019-09-17

Q

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  • 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

  • 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

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