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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

  • 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: 111
    • 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

  • 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: 71
    • 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

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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

  • 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: 9
    • 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

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  • 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

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  • 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: 49
    • 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
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  • Categories: Integration, Ordering, Visualisation
  • Added: 2021-12-10, Updated: 2023-07-21

  • Python implementation of the MNN batch correction algorithm
  • Platform: Python
  • Code: https://github.com/chriscainx/mnnpy
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  • 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
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  • License: MIT
  • Categories: Classification, Integration
  • Added: 2024-10-29, Updated: 2024-10-29

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  • 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: 12
    • 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
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  • License: MIT
  • Categories: Integration
  • Added: 2022-08-12, Updated: 2024-01-05

  • 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

  • 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

  • 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: 59
    • "A Python library for probabilistic analysis of single-cell omics data"
      DOI: 10.1038/s41587-021-01206-w, Published: 2022-02-07, Citations: 411
    • "Deep generative modeling for single-cell transcriptomics"
      DOI: 10.1038/s41592-018-0229-2, Published: 2018-11-30, Citations: 1632
    • "Joint probabilistic modeling of single-cell multi-omic data with totalVI"
      DOI: 10.1038/s41592-020-01050-x, Published: 2021-02-15, Citations: 368
    • "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: 153
    • "Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models"
      DOI: 10.15252/msb.20209620, Published: 2021-01-25, Citations: 375
    • 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: 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: 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

  • 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

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  • 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