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  • 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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    last commitUnknown
  • Categories: Quality Control
  • Added: 2021-01-22, Updated: 2021-07-27

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  • 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

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  • 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

  • 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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    last commitUnknown
  • License: BSD-3-Clause
  • Categories: Quality Control
  • Added: 2021-08-06, Updated: 2021-08-06

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