scRNA-tools

A

ATAXIC

An algorithm to quantify transcriptomic perturbation heterogeneity in single cancer cells.

Publications: 1, Preprints: 0, Total citations: 0

Publications
"ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells"
DOI: 10.1155/2022/4106736, Published: 2022-08-31, Citations: 0

Platform: R
Code: https://github.com/XS-Wang-Lab/ATAXIC
starsN/A
forksN/A
last commitUnknown
Categories: Perturbations
Added: 2022-09-09, Updated: 2022-09-09

Augur

Augur is an R package to prioritize cell types involved in the response to an experimental perturbation within high-dimensional single-cell data.

Publications: 2, Preprints: 1, Total citations: 198

Publications
"Cell type prioritization in single-cell data"
DOI: 10.1038/s41587-020-0605-1, Published: 2020-07-20, Citations: 164
"Prioritization of cell types responsive to biological perturbations in single-cell data with Augur"
DOI: 10.1038/s41596-021-00561-x, Published: 2021-06-25, Citations: 32
Preprints
"Cell type prioritization in single-cell data"
DOI: 10.1101/2019.12.20.884916, Citations: 2

Platform: R
Code: https://github.com/neurorestore/Augur
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Perturbations, Simulation, Variable Genes, Visualisation
Added: 2020-01-02, Updated: 2021-07-02

B

Beyondcell

Beyondcell is a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments

Publications: 1, Preprints: 1, Total citations: 63

Publications
"Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data"
DOI: 10.1186/s13073-021-01001-x, Published: 2021-12, Citations: 63
Preprints
"Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq"
DOI: 10.1101/2021.04.08.438954, Citations: 0

Platform: R
Code: https://gitlab.com/bu_cnio/beyondcell
License: GPL-2.0
Categories: Cell Cycle, Clustering, Dimensionality Reduction, Gene Sets, Perturbations, Visualisation
Added: 2021-04-12, Updated: 2021-12-17

C

CellCap

CellCap is a generative model of scRNA-seq perturbation data which emphasizes interpretability

Publications: 1, Preprints: 1, Total citations: 0

Publications
"Explainable modeling of single-cell perturbation data using attention and sparse dictionary learning"
DOI: 10.1016/j.cels.2025.101245, Published: 2025-04, Citations: 0
Preprints
"Modeling interpretable correspondence between cell state and perturbation response with CellCap"
DOI: 10.1101/2024.03.14.585078, Citations: 0

Platform: Python
Code: https://github.com/broadinstitute/CellCap
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Dimensionality Reduction, Perturbations
Added: 2024-03-22, Updated: 2025-04-13

CellDrift

A generalized linear model based functional data analysis approach to investigate temporal gene patterns in response to perturbations

Publications: 1, Preprints: 1, Total citations: 8

Publications
"CellDrift: inferring perturbation responses in temporally sampled single-cell data"
DOI: 10.1093/bib/bbac324, Published: 2022-08-23, Citations: 7
Preprints
"CellDrift: Inferring Perturbation Responses in Temporally-Sampled Single Cell Data"
DOI: 10.1101/2022.04.13.488194, Citations: 1

Platform: Python
Code: https://github.com/KANG-BIOINFO/CellDrift
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Ordering, Perturbations, Visualisation
Added: 2022-05-01, Updated: 2022-08-26

cellograph

Cellograph is a graph neural network model that uses a two-layer graph convolutional network to analyze single-cell RNA sequencing data

Publications: 1, Preprints: 1, Total citations: 5

Publications
"Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks"
DOI: 10.1186/s12859-024-05641-9, Published: 2024-01-15, Citations: 4
Preprints
"Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA-sequencing Data Using Graph Neural Networks"
DOI: 10.1101/2023.02.24.528672, Citations: 1

Platform: Python
Code: https://github.com/jashahir/cellograph
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Perturbations
Added: 2023-03-03, Updated: 2024-05-06

CellOracle

CellOracle integrates single-cell transcriptome and epigenome profiles to infer gene regulatory networks.

Publications: 2, Preprints: 1, Total citations: 419

Publications
"Gene regulatory network reconfiguration in direct lineage reprogramming"
DOI: 10.1016/j.stemcr.2022.11.010, Published: 2023-01, Citations: 23
"Dissecting cell identity via network inference and in silico gene perturbation"
DOI: 10.1038/s41586-022-05688-9, Published: 2023-02-08, Citations: 336
Preprints
"CellOracle: Dissecting cell identity via network inference and in silico gene perturbation"
DOI: 10.1101/2020.02.17.947416, Citations: 60

Platform: python
Code: https://github.com/morris-lab/CellOracle
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Gene Networks, Gene Sets, Integration, Perturbations, Simulation
Added: 2021-01-25, Updated: 2023-02-17

CellOT

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Publications: 1, Preprints: 1, Total citations: 71

Publications
"Learning single-cell perturbation responses using neural optimal transport"
DOI: 10.1038/s41592-023-01969-x, Published: 2023-09-28, Citations: 68
Preprints
"Learning Single-Cell Perturbation Responses using Neural Optimal Transport"
DOI: 10.21203/rs.3.rs-1805107/v1, Citations: 3

Platform: Python
Code: https://github.com/bunnech/cellot
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Perturbations, Visualisation
Added: 2022-08-12, Updated: 2024-01-05

CINEMA-OT

CINEMA-OT is a causal framework for perturbation effect analysis to identify individual treatment effects and synergy at the single cell level

Publications: 1, Preprints: 1, Total citations: 40

Publications
"Causal identification of single-cell experimental perturbation effects with CINEMA-OT"
DOI: 10.1038/s41592-023-02040-5, Published: 2023-11, Citations: 37
Preprints
"Causal identification of single-cell experimental perturbation effects with CINEMA-OT"
DOI: 10.1101/2022.07.31.502173, Citations: 3

Platform: Python
Code: https://github.com/vandijklab/CINEMA-OT
starsN/A
forksN/A
last commitUnknown
License: AGPL-3.0
Categories: Dimensionality Reduction, Perturbations
Added: 2022-12-02, Updated: 2024-01-05

contrastiveVI

Contrastive variational inference for single-cell transcriptomic data

Publications: 1, Preprints: 1, Total citations: 38

Publications
"Isolating salient variations of interest in single-cell data with contrastiveVI"
DOI: 10.1038/s41592-023-01955-3, Published: 2023-08-07, Citations: 31
Preprints
"Isolating salient variations of interest in single-cell transcriptomic data with contrastiveVI"
DOI: 10.1101/2021.12.21.473757, Citations: 7

Platform: Python
Code: https://github.com/suinleelab/contrastiveVI
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Dimensionality Reduction, Perturbations
Added: 2022-01-06, Updated: 2024-01-05

CoupleVAE

CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data

Publications: 1, Preprints: 1, Total citations: 0

Publications
"CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data"
DOI: 10.1093/bib/bbaf126, Published: 2025-03, Citations: 0
Preprints
"CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data"
DOI: 10.1101/2024.03.05.583614, Citations: 0

Platform: Python
Code: https://github.com/LiminLi-xjtu/CoupleVAE
starsN/A
forksN/A
last commitUnknown
Categories: Dimensionality Reduction, Perturbations
Added: 2024-03-15, Updated: 2025-04-13

CPA

CPA is a deep generative framework to learn effects of perturbations at the single-cell level

Publications: 1, Preprints: 1, Total citations: 167

Publications
"Predicting cellular responses to complex perturbations in high‐throughput screens"
DOI: 10.15252/msb.202211517, Published: 2023-05-08, Citations: 125
Preprints
"Compositional perturbation autoencoder for single-cell response modeling"
DOI: 10.1101/2021.04.14.439903, Citations: 42

Platform: Python
Code: https://github.com/facebookresearch/CPA
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Perturbations
Added: 2021-04-16, Updated: 2024-01-05

D

D-SPIN

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data

Publications: 0, Preprints: 1, Total citations: 8

Preprints
"D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response"
DOI: 10.1101/2023.04.19.537364, Citations: 8

Platform: Python/MATLAB
Code: https://github.com/JialongJiang/DSPIN
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Gene Networks, Perturbations, Visualisation
Added: 2023-06-02, Updated: 2023-06-02

DREEP

DREEP is a bioinformatics tool that leverages results from large-scale cell-line viability screens and enrichment analysis to predict drug vulnerability from the transcriptional state of a cell

Publications: 1, Preprints: 1, Total citations: 19

Publications
"Predicting drug response from single-cell expression profiles of tumours"
DOI: 10.1186/s12916-023-03182-1, Published: 2023-12-01, Citations: 18
Preprints
"Predicting drug response from single-cell expression profiles of tumours"
DOI: 10.1101/2023.06.01.543212, Citations: 1

Platform: C++/R
Code: https://github.com/gambalab/DREEP
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Clustering, Perturbations
Added: 2023-10-27, Updated: 2024-01-05

G

GEARS

GEARS, a method that can predict transcriptional response to both single and multi-gene perturbations using single-cell RNA-sequencing data from perturbational screens

Publications: 1, Preprints: 1, Total citations: 139

Publications
"Predicting transcriptional outcomes of novel multigene perturbations with GEARS"
DOI: 10.1038/s41587-023-01905-6, Published: 2023-08-17, Citations: 109
Preprints
"GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations"
DOI: 10.1101/2022.07.12.499735, Citations: 30

Platform: Python
Code: https://github.com/snap-stanford/GEARS
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Perturbations, Visualisation
Added: 2022-08-12, Updated: 2023-08-27

GenKI

A VGAE (Variational Graph Auto-Encoder) based model to learn perturbation using scRNA-seq data

Publications: 1, Preprints: 0, Total citations: 19

Publications
"Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks"
DOI: 10.1093/nar/gkad450, Published: 2023-05-29, Citations: 19

Platform: Python
Code: https://github.com/yjgeno/GenKI
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Gene Networks, Perturbations
Added: 2023-07-01, Updated: 2023-07-01

GSFA

Bayesian sparse factor analysis on single-cell CRISPR screen data

Publications: 1, Preprints: 1, Total citations: 22

Publications
"A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening"
DOI: 10.1038/s41592-023-02017-4, Published: 2023-09-28, Citations: 21
Preprints
"A novel Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening"
DOI: 10.1101/2022.02.13.480282, Citations: 1

Platform: R/C++
Code: https://github.com/xinhe-lab/GSFA
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Normalisation, Perturbations, Variable Genes, Variants
Added: 2022-02-18, Updated: 2023-10-27

I

ICAT

ICAT is a tool developed to better identify cell states in scRNAseq experiments where perturbations or some other biologic heterogeneity is present, such as gene knock-outs

Publications: 1, Preprints: 1, Total citations: 7

Publications
"ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes"
DOI: 10.1093/bioinformatics/btad278, Published: 2023-04-22, Citations: 7
Preprints
"ICAT: A Novel Algorithm to Robustly Identify Cell States Following Perturbations in Single Cell Transcriptomes"
DOI: 10.1101/2022.05.26.493603, Citations: 0

Platform: Python
Code: https://github.com/BradhamLab/icat
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Clustering, Perturbations
Added: 2022-06-03, Updated: 2023-05-12

M

MELD

MELD (Manifold Enhancement of Latent Dimensions), Quantifying the effect of experimental perturbations in scRNA-seq data.

Publications: 1, Preprints: 1, Total citations: 167

Publications
"Quantifying the effect of experimental perturbations at single-cell resolution"
DOI: 10.1038/s41587-020-00803-5, Published: 2021-02-08, Citations: 156
Preprints
"Quantifying the effect of experimental perturbations at single-cell resolution"
DOI: 10.1101/532846, Citations: 11

Platform: Python
Code: https://github.com/KrishnaswamyLab/MELD
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Integration, Perturbations
Added: 2019-02-01, Updated: 2021-03-19

MIMOSCA

Multiple Input Multiple Output Single Cell Analysis

Publications: 1, Preprints: 0, Total citations: 1432

Publications
"Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens"
DOI: 10.1016/j.cell.2016.11.038, Published: 2016-12, Citations: 1432

Platform: Python
Code: https://github.com/asncd/MIMOSCA
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Classification, Integration, Perturbations
Added: 2018-11-15, Updated: 2021-01-12

MUSIC

MUSIC: Model-based Understanding of SIngle-cell CRISPR screening

Publications: 1, Preprints: 0, Total citations: 76

Publications
"Model-based understanding of single-cell CRISPR screening"
DOI: 10.1038/s41467-019-10216-x, Published: 2019-05-20, Citations: 76

Platform: R
Code: https://github.com/bm2-lab/MUSIC
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Clustering, Perturbations, Quality Control, Visualisation
Added: 2019-05-31, Updated: 2021-01-12

mvTCR

A multimodal generative model to learn a unified representation across modalities and datasets for joint analysis of single-cell immune profiling data

Publications: 1, Preprints: 1, Total citations: 28

Publications
"Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data"
DOI: 10.1038/s41467-024-49806-9, Published: 2024-07-03, Citations: 12
Preprints
"Integrating T-cell receptor and transcriptome for large-scale single-cell immune profiling analysis"
DOI: 10.1101/2021.06.24.449733, Citations: 16

Platform: Python
Code: https://github.com/SchubertLab/mvTCR
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Immune, Integration, Perturbations
Added: 2022-10-28, Updated: 2025-04-13

O

OmicVerse

OmicVerse is the fundamental package for multi omics included bulk and single cell analysis with Python

Publications: 0, Preprints: 1, Total citations: 8

Preprints
"OmicVerse: A single pipeline for exploring the entire transcriptome universe"
DOI: 10.1101/2023.06.06.543913, Citations: 8

Platform: Python
Code: https://github.com/Starlitnightly/omicverse
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Classification, Gene Networks, Integration, Marker Genes, Ordering, Perturbations
Added: 2023-12-01, Updated: 2023-12-01

P

PACSI

PACSI is a novel approach that integrates scRNA-seq and bulk expression to identify cell subpopulations associated with disease phenotypes

Publications: 1, Preprints: 0, Total citations: 8

Publications
"Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI"
DOI: 10.1186/s12915-023-01658-3, Published: 2023-07-19, Citations: 8

Platform: R
Code: https://github.com/Chonghui-Liu/PACSI-project
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Perturbations
Added: 2023-07-28, Updated: 2023-07-28

PhEMD

PhEMD (phenotypic earth mover's distance) identifies axes of variation among multicellular biospecimens profiled at single-cell resolution.

Publications: 1, Preprints: 1, Total citations: 47

Publications
"Uncovering axes of variation among single-cell cancer specimens"
DOI: 10.1038/s41592-019-0689-z, Published: 2020-01-13, Citations: 47
Preprints
"Embedding single-cell experimental conditions to reveal manifold structure of cancer drug perturbation effects"
DOI: 10.1101/455436, Citations: 0

Platform: R
Code: https://github.com/KrishnaswamyLab/phemd
starsN/A
forksN/A
last commitUnknown
Categories: Dimensionality Reduction, Perturbations
Added: 2021-01-25, Updated: 2021-01-25

PopAlign

PopAlign constructs a compressed representation of single-cell data to identify subpopulations of cells within a heterogeneous mixture and tracks gene expression and cell abundance changes.

Publications: 1, Preprints: 1, Total citations: 33

Publications
"Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign"
DOI: 10.1073/pnas.2005990117, Published: 2020-10-30, Citations: 31
Preprints
"Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign"
DOI: 10.1101/421354, Citations: 2

Platform: python
Code: https://github.com/thomsonlab/popalign
starsN/A
forksN/A
last commitUnknown
Categories: Clustering, Differential Expression, Dimensionality Reduction, Perturbations
Added: 2021-01-25, Updated: 2021-01-25

PRESCIENT

PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative model for modeling single-cell time-series

Publications: 1, Preprints: 1, Total citations: 56

Publications
"Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions"
DOI: 10.1038/s41467-021-23518-w, Published: 2021-05-28, Citations: 55
Preprints
"Generative modeling of single-cell population time series for inferring cell differentiation landscapes"
DOI: 10.1101/2020.08.26.269332, Citations: 1

Platform: Python
Code: https://github.com/gifford-lab/prescient
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Ordering, Perturbations
Added: 2020-09-10, Updated: 2021-06-04

S

sc2MeNetDrug

A user-friendly computational tool for analyzing scRNA-seq data to discover inter-cell signaling communication and drugs

Publications: 1, Preprints: 1, Total citations: 0

Publications
"sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data"
DOI: 10.1371/journal.pcbi.1011785, Published: 2024-01-05, Citations: 0
Preprints
"sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data"
DOI: 10.1101/2021.11.15.468755, Citations: 0

Platform: R/JavaScript
Code: https://github.com/fuhaililab/sc2MeNetDrug
starsN/A
forksN/A
last commitUnknown
Categories: Clustering, Dimensionality Reduction, Gene Networks, Gene Sets, Imputation, Marker Genes, Normalisation, Perturbations, Quality Control
Added: 2023-10-27, Updated: 2024-05-06

SCATTome

SCATTome (Single Cell Analysis of Targeted Transcriptome) predicts a drug response per cell using qRT-PCR data.

Publications: 1, Preprints: 0, Total citations: 62

Publications
"Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors"
DOI: 10.1038/leu.2015.361, Published: 2015-12-29, Citations: 62

Platform: R
Code: https://github.com/bvnlab/SCATTome
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Perturbations
Added: 2021-01-25, Updated: 2021-01-25

scButterfly

A versatile single-cell cross-modality translation method via dual-aligned variational autoencoders

Publications: 1, Preprints: 0, Total citations: 19

Publications
"scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders"
DOI: 10.1038/s41467-024-47418-x, Published: 2024-04-06, Citations: 19

Platform: Python
Code: https://github.com/BioX-NKU/scButterfly
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Integration, Normalisation, Perturbations, Quality Control, Visualisation
Added: 2024-04-12, Updated: 2024-04-12

scCross

A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data

Publications: 1, Preprints: 1, Total citations: 16

Publications
"scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration"
DOI: 10.1186/s13059-024-03338-z, Published: 2024-07-29, Citations: 15
Preprints
"scCross: Bridging Modalities in Single–cell Multi–omics – Seamless Integration, Cross–modal Synthesis, and In–silico Exploration"
DOI: 10.1101/2023.11.22.568376, Citations: 1

Platform: Python
Code: https://github.com/mcgilldinglab/scCross
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Integration, Perturbations, Simulation
Added: 2023-12-01, Updated: 2025-04-13

scDEAL

Deep Transfer Learning of Drug Sensitivity by Integrating Bulk and Single-cell RNA-seq data

Publications: 1, Preprints: 1, Total citations: 101

Publications
"Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data"
DOI: 10.1038/s41467-022-34277-7, Published: 2022-10-30, Citations: 98
Preprints
"Deep Transfer Learning of Drug Responses by Integrating Bulk and Single-cell RNA-seq data"
DOI: 10.1101/2021.08.01.454654, Citations: 3

Platform: Python
Code: https://github.com/OSU-BMBL/scDEAL
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Perturbations
Added: 2021-08-08, Updated: 2022-11-04

scDecouple

scDecouple is an R package that aims to decouple true cellular response from infected proportion bias in scCRISPR-seq

Publications: 1, Preprints: 1, Total citations: 1

Publications
"scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq"
DOI: 10.1093/bib/bbae011, Published: 2024-01-22, Citations: 1
Preprints
"scDecouple: Decoupling cellular response from infected proportion bias in scCRISPR-seq"
DOI: 10.1101/2023.01.31.526445, Citations: 0

Platform: R
Code: https://github.com/MengQiuchen/scDecouple
starsN/A
forksN/A
last commitUnknown
Categories: Gene Sets, Perturbations, Variants
Added: 2023-09-08, Updated: 2024-05-06

scDisInFact

scDisInFact is a single-cell data integration and condition effect prediction framework

Publications: 1, Preprints: 1, Total citations: 14

Publications
"scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data"
DOI: 10.1038/s41467-024-45227-w, Published: 2024-01-30, Citations: 13
Preprints
"scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data"
DOI: 10.1101/2023.05.01.538975, Citations: 1

Platform: Python
Code: https://github.com/ZhangLabGT/scDisInFact
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Integration, Perturbations
Added: 2023-05-28, Updated: 2024-05-06

scDist

Robust identification of perturbed cell types in single-cell RNA-seq data

Publications: 1, Preprints: 1, Total citations: 7

Publications
"Robust identification of perturbed cell types in single-cell RNA-seq data"
DOI: 10.1038/s41467-024-51649-3, Published: 2024-09-01, Citations: 4
Preprints
"Robust identification of perturbed cell types in single-cell RNA-seq data"
DOI: 10.1101/2023.05.06.539326, Citations: 3

Platform: R
Code: https://github.com/phillipnicol/scDist
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Perturbations, Simulation, Visualisation
Added: 2023-05-26, Updated: 2025-04-13

scDrug

The scDrug constructed a workflow for comprehensive analysis on single-cell RNA sequencing (scRNA-seq) data

Publications: 1, Preprints: 0, Total citations: 26

Publications
"scDrug: From single-cell RNA-seq to drug response prediction"
DOI: 10.1016/j.csbj.2022.11.055, Published: 2023, Citations: 26

Platform: Python
Code: https://github.com/ailabstw/scDrug
starsN/A
forksN/A
last commitUnknown
Categories: Perturbations
Added: 2022-12-09, Updated: 2022-12-09

scDrugPrio

scDrugPrio presents a framework for drug prioritisation and repositioning in immune-mediated inflammatory diseases

Publications: 1, Preprints: 1, Total citations: 5

Publications
"scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases"
DOI: 10.1186/s13073-024-01314-7, Published: 2024-03-20, Citations: 5
Preprints
"scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases"
DOI: 10.1101/2023.11.08.566249, Citations: 0

Platform: R
Code: https://github.com/SDTC-CPMed/scDrugPrio
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Gene Networks, Perturbations
Added: 2023-11-17, Updated: 2024-05-06

scGen

scGen is a generative model to predict single-cell perturbation response across cell types, studies and species

Publications: 1, Preprints: 1, Total citations: 450

Publications
"scGen predicts single-cell perturbation responses"
DOI: 10.1038/s41592-019-0494-8, Published: 2019-07-29, Citations: 437
Preprints
"Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species"
DOI: 10.1101/478503, Citations: 13

Platform: Python
Code: https://github.com/theislab/scgen
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Differential Expression, Integration, Perturbations
Added: 2019-08-05, Updated: 2021-01-12

SCING

SCING: Single Cell INtegrative Gene regulatory network inference elucidates robust, interpretable gene regulatory networks

Publications: 1, Preprints: 1, Total citations: 19

Publications
"SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics"
DOI: 10.1016/j.isci.2023.107124, Published: 2023-07, Citations: 18
Preprints
"SCING: Single Cell INtegrative Gene regulatory network inference elucidates robust, interpretable gene regulatory networks"
DOI: 10.1101/2022.09.07.506959, Citations: 1

Platform: Python
Code: https://github.com/XiaYangLabOrg/SCING
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Gene Networks, Gene Sets, Perturbations
Added: 2023-07-07, Updated: 2023-07-07

scINSIGHT

scINSIGHT for interpreting single cell gene expression in biologically heterogeneous data

Publications: 1, Preprints: 1, Total citations: 20

Publications
"scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data"
DOI: 10.1186/s13059-022-02649-3, Published: 2022-03-21, Citations: 19
Preprints
"scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data"
DOI: 10.1101/2021.10.13.464306, Citations: 1

Platform: R/C++
Code: https://github.com/Vivianstats/scINSIGHT
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Gene Sets, Perturbations
Added: 2021-10-18, Updated: 2022-04-30

scMAGeCK

scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq)

Publications: 2, Preprints: 2, Total citations: 67

Publications
"Decoding heterogeneous single-cell perturbation responses"
DOI: 10.1038/s41556-025-01626-9, Published: 2025-02-26, Citations: 3
"scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens"
DOI: 10.1186/s13059-020-1928-4, Published: 2020-01-24, Citations: 60
Preprints
"Decoding Heterogenous Single-cell Perturbation Responses"
DOI: 10.1101/2023.10.30.564796, Citations: 3
"Linking genotypes with multiple phenotypes in single-cell CRISPR screens"
DOI: 10.1101/658146, Citations: 1

Platform: R/Python
Code: https://bitbucket.org/weililab/scmageck
License: BSD-2-Clause
Categories: Perturbations, Quantification, Variants
Added: 2020-02-05, Updated: 2025-04-13

scMulan

A Multitask Generative Pre-trained Language Model for Single-Cell Analysis

Publications: 0, Preprints: 1, Total citations: 4

Preprints
"scMulan: a multitask generative pre-trained language model for single-cell analysis"
DOI: 10.1101/2024.01.25.577152, Citations: 4

Platform: Python
Code: https://github.com/SuperBianC/scMulan
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Classification, Dimensionality Reduction, Integration, Perturbations, Simulation
Added: 2024-02-09, Updated: 2024-06-07

scPharm

R package for pharmacological cell subpopulations identification, drug prioritization and potetial drug combinations identification from single-cell RNA-seq of tumour tissue

Publications: 1, Preprints: 1, Total citations: 1

Publications
"scPharm: Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers"
DOI: 10.1002/advs.202412419, Published: 2024-11-19, Citations: 0
Preprints
"scPharm: identifying pharmacological subpopulations of single cells for precision medicine in cancers"
DOI: 10.1101/2023.12.11.571182, Citations: 1

Platform: R/C++
Code: https://github.com/WangHYLab/scPharm
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Perturbations
Added: 2024-01-19, Updated: 2025-04-13

scPRAM

scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism
Platform: Python
Code: https://github.com/jiang-q19/scPRAM
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Perturbations, Quality Control
Added: 2024-01-05, Updated: 2024-01-05

scRank

scRank can rank and infer drug-responsive cell population towards in-silico drug perturbation using a target-perturbed gene regulatory network

Publications: 1, Preprints: 0, Total citations: 10

Publications
"scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network"
DOI: 10.1016/j.xcrm.2024.101568, Published: 2024-06, Citations: 10

Platform: R
Code: https://github.com/ZJUFanLab/scRank
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Gene Networks, Gene Sets, Perturbations, Visualisation
Added: 2025-03-08, Updated: 2025-03-08

SCREEN

Predicting single-cell gene expression perturbation responses using optimal transport

Publications: 1, Preprints: 0, Total citations: 1

Publications
"SCREEN: predicting single-cell gene expression perturbation responses via optimal transport"
DOI: 10.1007/s11704-024-31014-9, Published: 2024-03-22, Citations: 1

Platform: Python
Code: https://github.com/Califorya/SCREEN
starsN/A
forksN/A
last commitUnknown
Categories: Perturbations
Added: 2024-04-05, Updated: 2024-04-05

scShift

scShift is a framework that learns batch and biological patterns from atlas-level scRNA-seq data as well as perturbation scRNA-seq data

Publications: 0, Preprints: 1, Total citations: 0

Preprints
"Deep identifiable modeling of single-cell atlases enables zero-shot query of cellular states"
DOI: 10.1101/2023.11.11.566161, Citations: 0

Platform: Python
Code: https://github.com/MingzeDong/scShift
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Dimensionality Reduction, Integration, Perturbations
Added: 2023-11-17, Updated: 2023-11-17

scTenifoldKnk

Perform virtual knockout experiments on single-cell gene regulatory networks

Publications: 1, Preprints: 1, Total citations: 1

Publications
"scTenifoldKnk: An Efficient Virtual Knockout Tool for Gene Function Predictions via Single-Cell Gene Regulatory Network Perturbation"
DOI: 10.2139/ssrn.3872938, Published: 2021, Citations: 0
Preprints
"scTenifoldKnk: a machine learning workflow performing virtual knockout experiments on single-cell gene regulatory networks"
DOI: 10.1101/2021.03.22.436484, Citations: 1

Platform: R/Python/MATLAB
Code: https://github.com/cailab-tamu/scTenifoldKnk
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0-or-later
Categories: Gene Networks, Perturbations, Simulation
Added: 2021-03-26, Updated: 2021-07-27

scTenifoldpy

This package is a Python version of scTenifoldNet and scTenifoldKnk

Publications: 1, Preprints: 0, Total citations: 33

Publications
"scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data"
DOI: 10.1016/j.patter.2020.100139, Published: 2020-12, Citations: 33

Platform: Python
Code: https://github.com/qwerty239qwe/scTenifoldpy
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Gene Networks, Perturbations
Added: 2021-11-20, Updated: 2021-11-20

secDrug

Drug combination for Cancer treatment using set-cover algorithm

Publications: 1, Preprints: 0, Total citations: 11

Publications
"secDrug: a pipeline to discover novel drug combinations to kill drug-resistant multiple myeloma cells using a greedy set cover algorithm and single-cell multi-omics"
DOI: 10.1038/s41408-022-00636-2, Published: 2022-03-09, Citations: 11

Platform: R
Code: https://github.com/Ujjal-Mukherjee/secDrug
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Perturbations
Added: 2022-03-18, Updated: 2022-03-18

T

trVAE

trVAE is a deep generative model which learns mapping between multiple different styles (conditions)

Publications: 1, Preprints: 1, Total citations: 91

Publications
"Conditional out-of-distribution generation for unpaired data using transfer VAE"
DOI: 10.1093/bioinformatics/btaa800, Published: 2020-12-29, Citations: 91
Preprints
"Conditional out-of-sample generation for unpaired data using trVAE"
arXiv: 1910.01791, Citations: 0

Platform: Python
Code: https://github.com/theislab/trVAE
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Perturbations
Added: 2019-11-03, Updated: 2021-06-28

U

UNAGI

UNAGI: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Publications: 0, Preprints: 1, Total citations: 2

Preprints
"Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases"
DOI: 10.21203/rs.3.rs-3676579/v1, Citations: 2

Platform: Python
Code: https://github.com/mcgilldinglab/UNAGI
starsN/A
forksN/A
last commitUnknown
Categories: Dimensionality Reduction, Gene Networks, Integration, Perturbations
Added: 2024-01-05, Updated: 2024-01-05

Z

ZetaSuite

A Computational Method for Analyzing High-dimensional High-throughput Data

Publications: 1, Preprints: 1, Total citations: 1

Publications
"ZetaSuite: computational analysis of two-dimensional high-throughput data from multi-target screens and single-cell transcriptomics"
DOI: 10.1186/s13059-022-02729-4, Published: 2022-07-25, Citations: 1
Preprints
"ZetaSuite, A Computational Method for Analyzing Multi-dimensional High-throughput Data, Reveals Genes with Opposite Roles in Cancer Dependency"
DOI: 10.1101/2021.08.29.458095, Citations: 0

Platform: JavaScript/Shell/R/Perl
Code: https://github.com/YajingHao/ZetaSuite
starsN/A
forksN/A
last commitUnknown
Categories: Imputation, Normalisation, Perturbations, Quality Control, Visualisation
Added: 2022-08-19, Updated: 2022-08-19

Tools

View entries for individual tools

Sorting & Filters

A

Publications

Publications

Preprints

B

Publications

Preprints

C

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

D

Preprints

Publications

Preprints

G

Publications

Preprints

Publications

Publications

Preprints

I

Publications

Preprints

M

Publications

Preprints

Publications

Publications

Publications

Preprints

O

Preprints

P

Publications

Publications

Preprints

Publications

Preprints

Publications

Preprints

S

Publications

Preprints

Publications

Publications

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Preprints

Publications

Publications

Preprints

Publications

Preprints

Publications