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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

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  • 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

  • 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

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  • 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

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  • 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

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  • 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
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  • License: MIT
  • Categories: Classification, Integration
  • Added: 2024-10-29, Updated: 2024-10-29

N

  • 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
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  • License: MIT
  • Categories: Classification
  • Added: 2019-11-03, Updated: 2019-11-03

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  • 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

  • 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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  • License: GPL-3.0
  • Categories: Classification, Gene Sets
  • Added: 2021-06-04, Updated: 2021-06-04

  • 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
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  • License: MIT
  • Categories: Classification
  • Added: 2021-06-04, Updated: 2021-06-04

  • 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

  • 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

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