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  • 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: 819
    • 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 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: 30
  • 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

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  • 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
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  • License: GPL-3.0
  • Categories: Imputation
  • Added: 2020-06-18, Updated: 2020-06-19

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  • 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: 631
    • Preprints
    • "SAVER: Gene expression recovery for UMI-based single cell RNA sequencing"
      DOI: 10.1101/138677, Citations: 21
  • Platform: R
  • Code: https://github.com/mohuangx/SAVER
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  • License: GPL-2.0
  • Categories: Imputation
  • Added: 2017-06-23, Updated: 2018-06-27

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

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

  • scvi-tools (single-cell variational inference tools) is a package for probabilistic modeling of single-cell omics data, built on top of PyTorch and Anndata

    • Publications
    • "PeakVI: A deep generative model for single-cell chromatin accessibility analysis"
      DOI: 10.1016/j.crmeth.2022.100182, Published: 2022-03, Citations: 65
    • "A Python library for probabilistic analysis of single-cell omics data"
      DOI: 10.1038/s41587-021-01206-w, Published: 2022-02-07, Citations: 481
    • "Deep generative modeling for single-cell transcriptomics"
      DOI: 10.1038/s41592-018-0229-2, Published: 2018-11-30, Citations: 1758
    • "Joint probabilistic modeling of single-cell multi-omic data with totalVI"
      DOI: 10.1038/s41592-020-01050-x, Published: 2021-02-15, Citations: 401
    • "An empirical Bayes method for differential expression analysis of single cells with deep generative models"
      DOI: 10.1073/pnas.2209124120, Published: 2023-05-16, Citations: 31
    • "Interpretable factor models of single-cell RNA-seq via variational autoencoders"
      DOI: 10.1093/bioinformatics/btaa169, Published: 2020-03-16, Citations: 157
    • "Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models"
      DOI: 10.15252/msb.20209620, Published: 2021-01-25, Citations: 430
    • 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: 59
    • "MultiVI: deep generative model for the integration of multi-modal data"
      DOI: 10.1101/2021.08.20.457057, Citations: 51
    • "An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models"
      DOI: 10.1101/2022.05.27.493625, Citations: 5
    • "Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing"
      DOI: 10.1101/292037, Citations: 22
    • "Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models"
      DOI: 10.1101/532895, Citations: 20
    • "Interpretable factor models of single-cell RNA-seq via variational autoencoders"
      DOI: 10.1101/737601, Citations: 9
    • "Deep Generative Models for Detecting Differential Expression in Single Cells"
      DOI: 10.1101/794289, Citations: 15
    • "Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data"
      DOI: 10.1101/794875, Citations: 12
    • "A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements"
      arXiv: 1905.02269, Citations: 0
  • Platform: Python
  • Code: https://github.com/YosefLab/scvi-tools
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  • License: BSD-3-Clause
  • Categories: Classification, Differential Expression, Dimensionality Reduction, Imputation, Integration, Normalisation, Quality Control
  • Added: 2018-04-04, Updated: 2024-01-05

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  • 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

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