scRNA-tools

A

APL

APL is a package developed for computation of Association Plots, a method for visualization and analysis of single cell transcriptomics data

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

Publications
"Visualizing cluster-specific genes from single-cell transcriptomics data using Association Plots"
DOI: 10.1016/j.jmb.2022.167525, Published: 2022-03, Citations: 19
Preprints
"Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots"
DOI: 10.1101/2020.10.23.352096, Citations: 0

Platform: R/Python
Code: https://github.com/VingronLab/APL
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License: GPL-3.0
Categories: Gene Sets, Marker Genes, Visualisation
Added: 2022-03-18, Updated: 2022-04-30

AltAnalyze

AltAnalyze is a multi-functional and easy-to-use software package for automated single-cell and bulk gene and splicing analyses.

Publications: 2, Preprints: 1, Total citations: 547

Publications
"Single-cell analysis of mixed-lineage states leading to a binary cell fate choice"
DOI: 10.1038/nature19348, Published: 2016-08-31, Citations: 485
"cellHarmony: cell-level matching and holistic comparison of single-cell transcriptomes"
DOI: 10.1093/nar/gkz789, Published: 2019-09-16, Citations: 59
Preprints
"cellHarmony: Cell-level matching and holistic comparison of single-cell transcriptomes"
DOI: 10.1101/412080, Citations: 3

Platform: Python
Code: https://github.com/nsalomonis/altanalyze
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License: Apache-2.0
Categories: Alternative Splicing, Cell Cycle, Classification, Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Networks, Gene Sets, Interactive, Marker Genes, Normalisation, Quantification, Visualisation
Added: 2017-07-16, Updated: 2018-11-12

AnnSql

The AnnSQL package enables SQL-based queries on AnnData objects

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

Publications
"AnnSQL: a Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources"
DOI: 10.1093/bioadv/vbaf105, Published: 2024-12-26, Citations: 0
Preprints
"AnnSQL: A Python SQL-based package for large-scale single-cell genomics analysis on a laptop"
DOI: 10.1101/2024.11.02.621676, Citations: 0

Platform: Python
Code: https://github.com/ArpiarSaundersLab/annsql
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License: MIT
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Marker Genes, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2025-03-08, Updated: 2025-12-30

activeSVC

ActiveSVC selects features for large matrix data with reduced computational complexity or limited data acquisition. It approaches Sequential Feature Selection through an active learning strategy with a support vector machine classifier.

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

Publications
"Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM"
DOI: 10.1038/s43588-022-00263-8, Published: 2022-06, Citations: 21
Preprints
"Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data"
DOI: 10.1101/2021.06.15.448478, Citations: 1

Platform: Python
Code: https://github.com/xqchen/activeSVC
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License: MIT
Categories: Marker Genes, Variable Genes
Added: 2022-02-18, Updated: 2024-03-22

B

BUSseq

The BUSseq R package implements the BUSseq model to adjust single-cell RNA-sequencing data for batch effects when there are unknown cell types.

Publications: 1, Preprints: 1, Total citations: 23

Publications
"Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction"
DOI: 10.1038/s41467-020-16905-2, Published: 2020-07-01, Citations: 22
Preprints
"Flexible Experimental Designs for Valid Single-cell RNA-sequencing Experiments Allowing Batch Effects Correction"
DOI: 10.1101/533372, Citations: 1

Platform: R/C++
Code: https://github.com/songfd2018/BUSseq
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License: GPL-2.0-or-later
Categories: Clustering, Imputation, Integration, Marker Genes, Visualisation
Added: 2019-02-01, Updated: 2021-10-29

Bisque

An R toolkit for accurate and efficient estimation of cell composition ('decomposition') from bulk expression data with single-cell information.

Publications: 1, Preprints: 1, Total citations: 338

Publications
"Accurate estimation of cell composition in bulk expression through robust integration of single-cell information"
DOI: 10.1038/s41467-020-15816-6, Published: 2020-04-24, Citations: 335
Preprints
"Accurate estimation of cell composition in bulk expression through robust integration of single-cell information"
DOI: 10.1101/669911, Citations: 3

Platform: R
Code: https://github.com/cozygene/bisque
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License: GPL-3.0
Categories: Integration, Marker Genes, Simulation
Added: 2019-06-20, Updated: 2019-10-02

bigSCale

bigSCale is an analytical framework scalable to analyze millions of cells

Publications: 1, Preprints: 1, Total citations: 83

Publications
"bigSCale: an analytical framework for big-scale single-cell data"
DOI: 10.1101/gr.230771.117, Published: 2018-05-03, Citations: 77
Preprints
"bigSCale: An Analytical Framework for Big-Scale Single-Cell Data"
DOI: 10.1101/197244, Citations: 6

Platform: MATLAB
Code: https://github.com/dfajar2/bigSCale
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Categories: Clustering, Differential Expression, Marker Genes, Normalisation, Visualisation
Added: 2017-10-29, Updated: 2021-06-28

bigSCale2

bigSCale is a complete framework for the analysis and visualization of single cell data. It allows cluster, phenotype, perform pseudotime analysis, infer gene regulatory networks and reduce large datasets in smaller datasets with higher quality.

Publications: 2, Preprints: 2, Total citations: 289

Publications
"bigSCale: an analytical framework for big-scale single-cell data"
DOI: 10.1101/gr.230771.117, Published: 2018-05-03, Citations: 77
"Single-cell transcriptomics unveils gene regulatory network plasticity"
DOI: 10.1186/s13059-019-1713-4, Published: 2019-06-04, Citations: 205
Preprints
"bigSCale: An Analytical Framework for Big-Scale Single-Cell Data"
DOI: 10.1101/197244, Citations: 6
"Single-Cell Transcriptomics Unveils Gene Regulatory Network Plasticity"
DOI: 10.1101/446104, Citations: 1

Platform: R/C++
Code: https://github.com/iaconogi/bigSCale2
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License: GPL-2.0-or-later
Categories: Classification, Clustering, Dimensionality Reduction, Gene Networks, Marker Genes, Normalisation, Ordering, Variable Genes, Visualisation
Added: 2019-07-11, Updated: 2019-07-11

C

CACIMAR

CACIMAR: cross-species analysis of cell identities, markers and regulations using single-cell sequencing profiles

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

Publications
"CACIMAR: cross-species analysis of cell identities, markers, regulations, and interactions using single-cell RNA sequencing data"
DOI: 10.1093/bib/bbae283, Published: 2024-05-23, Citations: 4
Preprints
"CACIMAR: Cross-species Analysis of Cell Identities, Markers, Regulations and Interactions Using Single-cell RNA Sequencing Data"
DOI: 10.1101/2024.01.23.576964, Citations: 0

Platform: R
Code: https://github.com/jiang-junyao/CACIMAR
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License: MIT
Categories: Gene Networks, Integration, Marker Genes, Visualisation
Added: 2024-02-02, Updated: 2025-04-13

CIARA

CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types) is an R package that identifies potential markers of rare cell types looking at genes whose expression is confined in small regions of the expression space

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

Publications
"CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data"
DOI: 10.1242/dev.201264, Published: 2023-06-01, Citations: 7
Preprints
"CIARA: a cluster-independent algorithm for the identification of markers of rare cell types from single-cell RNA seq data"
DOI: 10.1101/2022.08.01.501965, Citations: 0

Platform: R
Code: https://github.com/ScialdoneLab/CIARA
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License: Artistic-2.0
Categories: Clustering, Marker Genes, Rare Cells, Visualisation
Added: 2022-08-19, Updated: 2024-01-05

CIPR

Cluster Identity PRedictor (CIPR) provides a graphical user interface to quickly score gene expression profiles of unknown cell clusters against mouse or human references

Publications: 1, Preprints: 0, Total citations: 68

Publications
"CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments"
DOI: 10.1186/s12859-020-3538-2, Published: 2020-05-15, Citations: 68

Platform: R
Code: https://github.com/atakanekiz/CIPR-Package
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License: GPL-3.0
Categories: Classification, Interactive, Marker Genes, Visualisation
Added: 2020-05-22, Updated: 2020-05-22

COMET

COMET: Identifying candidate marker panels from single-cell transcriptomic data

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

Publications
"Combinatorial prediction of marker panels from single‐cell transcriptomic data"
DOI: 10.15252/msb.20199005, Published: 2019-10-24, Citations: 88
Preprints
"Combinatorial prediction of marker panels from single-cell transcriptomic data"
DOI: 10.1101/655753, Citations: 3

Platform: Python
Code: https://github.com/MSingerLab/COMETSC
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License: BSD-3-Clause
Categories: Marker Genes, Visualisation
Added: 2019-05-31, Updated: 2019-11-12

CONCLUS

CONCLUS is a tool for robust clustering and positive marker features selection of single-cell RNA-seq (sc-RNA-seq) datasets.

Publications: 0, Preprints: 1, Total citations: 5

Preprints
"Single-cell atlas of major haematopoietic tissues sheds light on blood cell formation from embryonic endothelium"
DOI: 10.1101/774547, Citations: 5

Platform: R
Code: https://github.com/lancrinlab/CONCLUS
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License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Marker Genes, Normalisation, Rare Cells, Visualisation
Added: 2019-12-10, Updated: 2021-05-21

COSG

COSG is a cosine similarity-based method for more accurate and scalable marker gene identification

Publications: 1, Preprints: 1, Total citations: 124

Publications
"Accurate and fast cell marker gene identification with COSG"
DOI: 10.1093/bib/bbab579, Published: 2022-01-19, Citations: 123
Preprints
"Accurate and fast cell marker gene identification with COSG"
DOI: 10.1101/2021.06.15.448484, Citations: 1

Platform: Python
Code: https://github.com/genecell/COSG
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License: BSD-3-Clause
Categories: Marker Genes
Added: 2021-06-18, Updated: 2022-03-04

COSGR

R implementation of COSG

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

Preprints
"Accurate and fast cell marker gene identification with COSG"
DOI: 10.1101/2021.06.15.448484, Citations: 1

Platform: R
Code: https://github.com/genecell/COSGR
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License: BSD-3-Clause
Categories: Marker Genes
Added: 2021-06-18, Updated: 2021-06-18

COT

Cosine based One-sample Test (COT) is an accurate and efficient method to detect subtype-specific marker genes

Publications: 1, Preprints: 1, Total citations: 15

Publications
"COT: an efficient and accurate method for detecting marker genes among many subtypes"
DOI: 10.1093/bioadv/vbac037, Published: 2022-01-01, Citations: 15
Preprints
"COT: an efficient Python tool for detecting marker genes among many subtypes"
DOI: 10.1101/2021.01.10.426146, Citations: 0

Platform: Python
Code: https://github.com/MintaYLu/COT
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License: MIT
Categories: Marker Genes
Added: 2021-01-15, Updated: 2022-06-17

CellAnnotator

CellAnnotator is an scverse ecosystem package, designed to annotate cell types in scRNA-seq data based on marker genes using OpenAI models
Platform: Python
Code: https://github.com/quadbio/cell-annotator
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License: MIT
Categories: Classification, Marker Genes
Added: 2025-08-02, Updated: 2025-08-02

CellBridge

CellBridge is an automated and versatile workflow meticulously designed to simplify and expedite the standard procedures entailed in scRNA-seq analysis

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

Publications
"Scaling up single-cell RNA-seq data analysis with CellBridge workflow"
DOI: 10.1093/bioinformatics/btad760, Published: 2023-12-19, Citations: 10

Platform: R/Python/Docker
License: Custom
Categories: Clustering, Marker Genes, Quality Control, Visualisation
Added: 2024-01-05, Updated: 2024-09-15

chevreulProcess

Tools analyzing SingleCellExperiment objects as projects. for input into the Chevreul app downstream. Includes functions for analysis of single cell RNA sequencing data.
Platform: R
Code: https://github.com/whtns/chevreulProcess
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License: MIT
Categories: Cell Cycle, Clustering, Dimensionality Reduction, Integration, Marker Genes, Normalisation, Quality Control
Added: 2025-04-24, Updated: 2025-04-24

clusterExperiment

Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot.

Publications: 1, Preprints: 1, Total citations: 55

Publications
"clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets"
DOI: 10.1371/journal.pcbi.1006378, Published: 2018-09-04, Citations: 53
Preprints
"clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets"
DOI: 10.1101/280545, Citations: 2

Platform: R
Code: https://github.com/epurdom/clusterExperiment
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License: Artistic-2.0
Categories: Clustering, Marker Genes, Visualisation
Added: 2016-09-08, Updated: 2020-03-25

cluster_diffex

Representative marker selection, cluster, visualization, and binomial differential expression pipeline

Publications: 1, Preprints: 1, Total citations: 222

Publications
"Single-cell transcriptome analysis of lineage diversity in high-grade glioma"
DOI: 10.1186/s13073-018-0567-9, Published: 2018-07-24, Citations: 219
Preprints
"Single-Cell Transcriptome Analysis of Lineage Diversity and Microenvironment in High-Grade Glioma"
DOI: 10.1101/250704, Citations: 3

Platform: Python
Code: https://github.com/simslab/cluster_diffex2018
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License: MIT
Categories: Clustering, Differential Expression, Dimensionality Reduction, Marker Genes, Visualisation
Added: 2020-04-28, Updated: 2020-04-28

clustermole

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

clusterpval

clusterpval is an R package that tests the null hypothesis of no difference in means between two estimated clusters in a data set

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

Preprints
"Selective Inference for Hierarchical Clustering"
arXiv: 2012.02936, Citations: 0

Platform: R
Code: https://github.com/lucylgao/clusterpval
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License: MIT
Categories: Marker Genes
Added: 2020-12-13, Updated: 2020-12-13

CellRanger

Cell Ranger is a set of analysis pipelines that process Chromium single cell 3’ RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis.

Publications: 1, Preprints: 1, Total citations: 6340

Publications
"Massively parallel digital transcriptional profiling of single cells"
DOI: 10.1038/ncomms14049, Published: 2017-01-16, Citations: 6314
Preprints
"Massively parallel digital transcriptional profiling of single cells"
DOI: 10.1101/065912, Citations: 26

Platform: Python/R
Code: https://github.com/10XGenomics/cellranger
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Categories: Alignment, Clustering, Differential Expression, Dimensionality Reduction, Interactive, Marker Genes, Quality Control, Quantification, UMIs, Visualisation
Added: 2017-06-08, Updated: 2018-04-09

CellRouter

CellRouter is a multifaceted single-cell analysis platform that identifies complex cell-state transition trajectories by using flow networks to explore the subpopulation structure of multi-dimensional, single-cell omics data.

Publications: 1, Preprints: 0, Total citations: 87

Publications
"Reconstruction of complex single-cell trajectories using CellRouter"
DOI: 10.1038/s41467-018-03214-y, Published: 2018-03-01, Citations: 87

Platform: R
Code: https://github.com/edroaldo/cellrouter
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Categories: Clustering, Dimensionality Reduction, Expression Patterns, Marker Genes, Normalisation, Ordering, Quality Control, Visualisation
Added: 2018-03-14, Updated: 2018-10-02

CellTICS

CellTICS is a biologically interpretable neural network for (sub-) cell-type identification and interpretation based on single-cell RNA-seq data

Publications: 1, Preprints: 0, Total citations: 12

Publications
"CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data"
DOI: 10.1093/bib/bbad449, Published: 2023-12-07, Citations: 12

Platform: Python
Code: https://github.com/qyyin0516/CellTICS
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Categories: Classification, Gene Sets, Marker Genes
Added: 2024-01-05, Updated: 2024-01-05

Cepo

A method to explore cell identities from single-cell RNA-sequencing data using differential stability

Publications: 1, Preprints: 1, Total citations: 30

Publications
"Uncovering cell identity through differential stability with Cepo"
DOI: 10.1038/s43588-021-00172-2, Published: 2021-12, Citations: 28
Preprints
"Cepo uncovers cell identity through differential stability"
DOI: 10.1101/2021.01.10.426138, Citations: 2

Platform: R
Code: https://github.com/PYangLab/Cepo
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License: MIT
Categories: Differential Expression, Marker Genes, Visualisation
Added: 2021-01-15, Updated: 2022-01-06

ClonoCluster

A method for using clonal origin to inform transcriptome clustering

Publications: 1, Preprints: 1, Total citations: 17

Publications
"ClonoCluster: A method for using clonal origin to inform transcriptome clustering"
DOI: 10.1016/j.xgen.2022.100247, Published: 2023-02, Citations: 14
Preprints
"ClonoCluster: a method for using clonal origin to inform transcriptome clustering"
DOI: 10.1101/2022.02.11.480077, Citations: 3

Platform: R
Code: https://github.com/leeprichman/ClonoCluster
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License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Marker Genes, Variants, Visualisation
Added: 2022-02-18, Updated: 2023-05-12

ClusterDE

A Post-clustering Differential Expression (DE) Method For Solving Double Dipping

Publications: 1, Preprints: 2, Total citations: 20

Publications
"Synthetic Control Removes Spurious Discoveries from Double Dipping in Single-Cell and Spatial Transcriptomics Data Analyses"
DOI: 10.1007/978-3-031-90252-9_48, Published: 2025, Citations: 1
Preprints
"ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping"
DOI: 10.1101/2023.07.21.550107, Citations: 14
"ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping"
DOI: 10.21203/rs.3.rs-3211191/v1, Citations: 5

Platform: R
Code: https://github.com/SONGDONGYUAN1994/ClusterDE
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License: MIT
Categories: Differential Expression, Marker Genes
Added: 2023-08-04, Updated: 2025-12-30

CombiROC

CombiROC is a totally new music in multi-markers analysis: an R package for efficient and easy combinatorial selection of biomarkers and sensitivity/specificity-driven prioritization of features.

Publications: 0, Preprints: 1, Total citations: 6

Preprints
"Combinatorial selection of biomarkers to optimize gene signatures in diagnostics and single cell applications"
DOI: 10.1101/2022.01.17.476603, Citations: 6

Platform: R
Code: https://github.com/ingmbioinfo/combiroc
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License: MIT
Categories: Marker Genes
Added: 2022-01-22, Updated: 2022-01-22

Conos

Clustering on Network of Samples

Publications: 1, Preprints: 1, Total citations: 297

Publications
"Joint analysis of heterogeneous single-cell RNA-seq dataset collections"
DOI: 10.1038/s41592-019-0466-z, Published: 2019-07-15, Citations: 281
Preprints
"Wiring together large single-cell RNA-seq sample collections"
DOI: 10.1101/460246, Citations: 16

Platform: R/C++
Code: https://github.com/hms-dbmi/conos
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License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Integration, Marker Genes, Visualisation
Added: 2018-11-09, Updated: 2021-02-26

countland

Tools for analyzing biological count data, especially from single cell RNA-seq

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

Publications
"Normalizing need not be the norm: count-based math for analyzing single-cell data"
DOI: 10.1007/s12064-023-00408-x, Published: 2023-11-10, Citations: 3
Preprints
"Normalizing need not be the norm: count-based math for analyzing single-cell data"
DOI: 10.1101/2022.06.01.494334, Citations: 5

Platform: Python/R
Code: https://github.com/shchurch/countland
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License: MIT
Categories: Clustering, Marker Genes, Quality Control, Visualisation
Added: 2022-06-03, Updated: 2024-01-05

cytocipher

Cytocipher - detection of significantly different cell populations in scRNA-seq

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

Publications
"Cytocipher determines significantly different populations of cells in single cell RNA-seq data"
DOI: 10.1093/bioinformatics/btad435, Published: 2023-07-14, Citations: 2
Preprints
"Cytocipherdetermines significantly different populations of cells in single cell RNA-seq data"
DOI: 10.1101/2022.08.12.503759, Citations: 0

Platform: Python
Code: https://github.com/BradBalderson/Cytocipher
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License: GPL-3.0
Categories: Clustering, Marker Genes, Visualisation
Added: 2022-10-28, Updated: 2023-07-21

D

DElegate

Wrapper and helper functions to use bulk RNA-seq differential expression methods with single-cell data

Publications: 0, Preprints: 1, Total citations: 30

Preprints
"Single-cell RNA-seq differential expression tests within a sample should use pseudo-bulk data of pseudo-replicates"
DOI: 10.1101/2023.03.28.534443, Citations: 30

Platform: R
Code: https://github.com/cancerbits/DElegate
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License: GPL-3.0
Categories: Differential Expression, Marker Genes
Added: 2023-03-31, Updated: 2023-03-31

DPre

Computational identification of differentiation bias and genes underlaying cell type conversions

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

Publications
"DPre: Computational identification of differentiation bias and genes underlying cell type conversions"
DOI: 10.1093/bioinformatics/btz789, Published: 2019-10-17, Citations: 2

Platform: Python
Code: https://github.com/LoaloaF/DPre
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License: MIT
Categories: Gene Sets, Marker Genes, Visualisation
Added: 2019-10-24, Updated: 2019-10-24

DepecheR

DepecheR is an R package for clustering cytometry and single-cell RNA sequencing data according to a minimal set of biological markers

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

Publications
"Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE"
DOI: 10.1371/journal.pone.0203247, Published: 2019-03-07, Citations: 19
Preprints
"Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE"
DOI: 10.1101/396135, Citations: 0

Platform: R
Code: https://github.com/Theorell/DepecheR
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License: MIT
Categories: Clustering, Marker Genes, Visualisation
Added: 2018-11-14, Updated: 2018-11-14

E

ENCORE

ENtropy subspace separation-based Clustering for nOise REduction (ENCORE)

Publications: 1, Preprints: 0, Total citations: 18

Publications
"Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data"
DOI: 10.1093/nar/gkaa1157, Published: 2020-12-10, Citations: 18

Platform: R
Code: https://github.com/SONG0417/ENCORE_V1.0
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License: GPL-3.0
Categories: Clustering, Marker Genes
Added: 2021-01-08, Updated: 2021-06-28

EasyCellType

EasyCellType is a R package developed to automatically examine the input marker lists obtained from existing software such as Seurat over the cell marker databases

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

Publications
"EasyCellType: marker-based cell-type annotation by automatically querying multiple databases"
DOI: 10.1093/bioadv/vbad029, Published: 2023-03-24, Citations: 8

Platform: R
Code: https://github.com/rx-li/EasyCellType
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License: Artistic-2.0
Categories: Classification, Marker Genes, Visualisation
Added: 2022-11-04, Updated: 2023-03-31

G

GEMLI

GEMLI is an R package to predict cell lineages (cells with a common ancestor) from single cell RNA datasets (or any other type of single-cell gene expression data) and to call genes with a high gene expression memory on the predicted cell lineages

Publications: 1, Preprints: 0, Total citations: 9

Publications
"Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets"
DOI: 10.1038/s41467-024-47158-y, Published: 2024-03-29, Citations: 9

Platform: R
Code: https://github.com/UPSUTER/GEMLI
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last commitUnknown
Categories: Differential Expression, Expression Patterns, Marker Genes, Ordering, Visualisation
Added: 2024-04-05, Updated: 2024-04-05

Garnett

Garnett, an algorithm and accompanying software for rapidly annotating cell types in scRNA-seq and scATAC-seq datasets, based on an interpretable, hierarchical markup language of cell type-specific genes.

Publications: 1, Preprints: 1, Total citations: 443

Publications
"Supervised classification enables rapid annotation of cell atlases"
DOI: 10.1038/s41592-019-0535-3, Published: 2019-09-09, Citations: 427
Preprints
"Supervised classification enables rapid annotation of cell atlases"
DOI: 10.1101/538652, Citations: 16

Platform: R
Code: https://github.com/cole-trapnell-lab/garnett
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License: MIT
Categories: Classification, Marker Genes, Visualisation
Added: 2019-02-11, Updated: 2019-09-17

GenePattern

Interactive notebook workflow for pre-processing data, identification of sub-populations of cells by clustering, and exploration of biomarkers.

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

Publications
"An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data"
DOI: 10.12688/f1000research.15830.1, Published: 2018-08-16, Citations: 4

Platform: Python
Code: https://github.com/genepattern/single_cell_clustering_notebook
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License: BSD-3-Clause
Categories: Clustering, Dimensionality Reduction, Interactive, Marker Genes, Normalisation, Quality Control, Visualisation
Added: 2018-08-24, Updated: 2019-10-02

geneBasisR

Selects an optimal targeted gene panel (based on scRNA-seq data) as a function of designated number of genes

Publications: 1, Preprints: 1, Total citations: 39

Publications
"geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq"
DOI: 10.1186/s13059-021-02548-z, Published: 2021-12, Citations: 39
Preprints
"geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq."
DOI: 10.1101/2021.08.10.455720, Citations: 0

Platform: R
Code: https://github.com/MarioniLab/geneBasisR
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License: MIT
Categories: Differential Expression, Dimensionality Reduction, Marker Genes, Visualisation
Added: 2021-08-11, Updated: 2021-12-17

genesorteR

genesorteR is an R package for single cell data analysis. It calculates a specificity score to rank all genes in each cell cluster. It can then use this ranking to find sets of marker genes or to find highly variable genes.

Publications: 0, Preprints: 1, Total citations: 25

Preprints
"genesorteR: Feature Ranking in Clustered Single Cell Data"
DOI: 10.1101/676379, Citations: 25

Platform: R
Code: https://github.com/mahmoudibrahim/genesorteR
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License: GPL-3.0
Categories: Marker Genes, Visualisation
Added: 2017-06-28, Updated: 2017-06-28

H

Hotspot

Hotspot is a tool for identifying informative genes (and gene modules) in a single-cell dataset

Publications: 1, Preprints: 1, Total citations: 218

Publications
"Hotspot identifies informative gene modules across modalities of single-cell genomics"
DOI: 10.1016/j.cels.2021.04.005, Published: 2021-05, Citations: 206
Preprints
"Identifying Informative Gene Modules Across Modalities of Single Cell Genomics"
DOI: 10.1101/2020.02.06.937805, Citations: 12

Platform: Python
Code: https://github.com/Yoseflab/Hotspot
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License: MIT
Categories: Gene Sets, Marker Genes, Visualisation
Added: 2020-02-20, Updated: 2022-04-30

hsslda

LDA with Hybrid Subset Selection

Publications: 1, Preprints: 1, Total citations: 25

Publications
"Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA"
DOI: 10.1016/j.patter.2022.100536, Published: 2022-06, Citations: 24
Preprints
"Supervised dimensionality reduction for exploration of single-cell data by Hybrid Subset Selection - Linear Discriminant Analysis"
DOI: 10.1101/2022.01.06.475279, Citations: 1

Platform: R
Code: https://github.com/mamouzgar/hsslda
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License: GPL-3.0
Categories: Dimensionality Reduction, Marker Genes
Added: 2022-01-14, Updated: 2022-07-15

I

ICARUS

Welcome to ICARUS (Interactive single Cell RNA-seq Analysis with R shiny Using Seurat)

Publications: 2, Preprints: 1, Total citations: 48

Publications
"ICARUS, an interactive web server for single cell RNA-seq analysis"
DOI: 10.1093/nar/gkac322, Published: 2022-05-10, Citations: 42
"Delineation of complex gene expression patterns in single cell RNA-seq data with ICARUS v2.0"
DOI: 10.1093/nargab/lqad032, Published: 2022-06-01, Citations: 5
Preprints
"Delineation of complex gene expression patterns in single cell RNA-seq data with ICARUS v2.0"
DOI: 10.1101/2023.01.23.525100, Citations: 1

Platform: R
Code: https://github.com/Enjewl/ICARUS
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License: MIT
Categories: Cell Cycle, Classification, Differential Expression, Dimensionality Reduction, Gene Networks, Gene Sets, Imputation, Interactive, Marker Genes, Quality Control, Variants, Visualisation
Added: 2023-01-27, Updated: 2023-05-12

IKAP

IKAP – Identifying K mAjor cell Population groups in single-cell RNA-seq analysis

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

Publications
"IKAP—Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis"
DOI: 10.1093/gigascience/giz121, Published: 2019-10-01, Citations: 20
Preprints
"IKAP - Identifying K mAjor cell Population groups in single-cell RNA-seq analysis"
DOI: 10.1101/596817, Citations: 2

Platform: R
Code: https://github.com/NHLBI-BCB/IKAP
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last commitUnknown
License: MIT
Categories: Clustering, Marker Genes, Visualisation
Added: 2019-10-09, Updated: 2019-10-09

iCellR

iCellR is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq and CITE-seq)

Publications: 0, Preprints: 2, Total citations: 12

Preprints
"iCellR: Combined Coverage Correction and Principal Component Alignment for Batch Alignment in Single-Cell Sequencing Analysis"
DOI: 10.1101/2020.03.31.019109, Citations: 9
"Graph Drawing-based Dimensionality Reduction to Identify Hidden Communities in Single-Cell Sequencing Spatial Representation"
DOI: 10.1101/2020.05.05.078550, Citations: 3

Platform: R
Code: https://github.com/rezakj/iCellR
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License: GPL-2.0
Categories: Cell Cycle, Clustering, Differential Expression, Dimensionality Reduction, Imputation, Integration, Marker Genes, Normalisation, Quality Control, Visualisation
Added: 2020-04-13, Updated: 2020-05-08

iasva

Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated with the biological variable of interest.

Publications: 1, Preprints: 1, Total citations: 9

Publications
"Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA)"
DOI: 10.1038/s41598-018-35365-9, Published: 2018-11-19, Citations: 8
Preprints
"A statistical framework for the robust detection of hidden variation in single cell transcriptomes"
DOI: 10.1101/151217, Citations: 1

Platform: R
Code: https://github.com/UcarLab/iasva
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last commitUnknown
License: GPL-2.0
Categories: Dimensionality Reduction, Marker Genes, Normalisation
Added: 2018-03-15, Updated: 2018-12-01

immunopipe

Integrative analysis for single-cell RNA sequencing and single-cell TCR sequencing data
Platform: Python/R/Shell
Code: https://github.com/pwwang/immunopipe
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Gene Sets, Immune, Integration, Marker Genes, Visualisation
Added: 2024-04-05, Updated: 2024-04-05

L

LIGER

LIGER (Linked Inference of Genomic Experimental Relationships)

Publications: 3, Preprints: 3, Total citations: 1446

Publications
"Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity"
DOI: 10.1016/j.cell.2019.05.006, Published: 2019-06, Citations: 1168
"UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization"
DOI: 10.1038/s41467-022-28431-4, Published: 2022-02-09, Citations: 69
"Jointly defining cell types from multiple single-cell datasets using LIGER"
DOI: 10.1038/s41596-020-0391-8, Published: 2020-10-12, Citations: 181
Preprints
"Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGER"
DOI: 10.1101/2020.04.07.029546, Citations: 1
"Nonnegative matrix factorization integrates single-cell multi-omic datasets with partially overlapping features"
DOI: 10.1101/2021.04.09.439160, Citations: 4
"Integrative inference of brain cell similarities and differences from single-cell genomics"
DOI: 10.1101/459891, Citations: 23

Platform: R/C++
Code: https://github.com/welch-lab/liger
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last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Integration, Marker Genes, Normalisation, Variable Genes, Visualisation
Added: 2018-11-09, Updated: 2025-06-17

LINEAGE

Label-free IdeNtification of Endogenous informAtive sinGle cell mitochondrial RNA mutation for lineage analysis and clonal Evolution (LINEAGE)

Publications: 1, Preprints: 0, Total citations: 13

Publications
"LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis"
DOI: 10.1073/pnas.2119767119, Published: 2022-01-27, Citations: 13

Platform: R
Code: https://github.com/songjiajia2018/LINEAGE
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last commitUnknown
License: GPL-3.0
Categories: Marker Genes, Ordering, Variants
Added: 2022-02-04, Updated: 2022-02-04

M

M3Drop

R package providing functions for fitting a modified Michaelis-Menten (MM) equation to the pattern of dropouts observed in a single-cell sequencing experiment.

Publications: 1, Preprints: 1, Total citations: 237

Publications
"M3Drop: dropout-based feature selection for scRNASeq"
DOI: 10.1093/bioinformatics/bty1044, Published: 2018-12-24, Citations: 222
Preprints
"Dropout-based feature selection for scRNASeq"
DOI: 10.1101/065094, Citations: 15

Platform: R
Code: https://github.com/tallulandrews/M3Drop
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last commitUnknown
License: GPL-2.0-or-later
Categories: Differential Expression, Marker Genes, Simulation, Visualisation
Added: 2016-10-10, Updated: 2019-01-06

MASI

MASI: marker-assisted standardization and integration for single-cell transcriptomics data

Publications: 1, Preprints: 1, Total citations: 3

Publications
"MASI enables fast model-free standardization and integration of single-cell transcriptomics data"
DOI: 10.1038/s42003-023-04820-3, Published: 2023-04-28, Citations: 3
Preprints
"Fast model-free standardization and integration of single-cell transcriptomics data"
DOI: 10.1101/2022.03.28.486110, Citations: 0

Platform: Python
Code: https://github.com/hayatlab/MASI
starsN/A
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last commitUnknown
Categories: Classification, Marker Genes
Added: 2023-05-12, Updated: 2023-05-12

MGFR

A package for marker gene detection

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

Publications
"Detection of condition-specific marker genes from RNA-seq data with MGFR"
DOI: 10.7717/peerj.6970, Published: 2019-05-27, Citations: 11

Platform: R
License: GPL-3.0
Categories: Marker Genes
Added: 2019-06-06, Updated: 2019-06-06

MICTI

Marker gene Identification for Cell Type Identity

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

Preprints
"Feature extraction approach in single-cell gene expression profiling for cell-type marker identification"
DOI: 10.1101/686659, Citations: 1

Platform: Python
Code: https://github.com/insilicolife/micti
starsN/A
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last commitUnknown
License: MIT
Categories: Marker Genes, Visualisation
Added: 2019-07-04, Updated: 2019-07-04

MUDAN

Multi-sample Unified Discriminant ANalysis
Platform: R
Code: https://github.com/JEFworks/MUDAN
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Marker Genes, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2018-02-13, Updated: 2022-05-20

mLLMCelltype

An iterative multi-LLM consensus framework for cell type annotation in single-cell RNA sequencing data. By leveraging multiple large language models (GPT, Claude, Gemini, Qwen), it significantly improves annotation accuracy while providing transparent uncertainty quantification.

Publications: 0, Preprints: 1, Total citations: 3

Preprints
"Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data"
DOI: 10.1101/2025.04.10.647852, Citations: 3

Platform: R/Python
Code: https://github.com/cafferychen777/mLLMCelltype
starsN/A
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last commitUnknown
License: MIT
Categories: Classification, Interactive, Marker Genes
Added: 2025-04-18, Updated: 2025-04-24

mastR

An R package for automatically screening group specific signature for specific tissues

Publications: 1, Preprints: 1, Total citations: 3

Publications
"mastR: an R package for automated identification of tissue-specific gene signatures in multi-group differential expression analysis"
DOI: 10.1093/bioinformatics/btaf114, Published: 2025-03, Citations: 2
Preprints
"mastR: Marker Automated Screening Tool for multi-omics data"
DOI: 10.1101/2024.04.24.590691, Citations: 1

Platform: R
Code: https://github.com/DavisLaboratory/mastR
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last commitUnknown
License: MIT
Categories: Differential Expression, Gene Sets, Marker Genes, Visualisation
Added: 2024-05-06, Updated: 2025-04-13

miloR

Milo is a method for differential abundance analysis on KNN graph from single-cell datasets

Publications: 1, Preprints: 2, Total citations: 712

Publications
"Differential abundance testing on single-cell data using k-nearest neighbor graphs"
DOI: 10.1038/s41587-021-01033-z, Published: 2021-09-30, Citations: 680
Preprints
"Milo: differential abundance testing on single-cell data using k-NN graphs"
DOI: 10.1101/2020.11.23.393769, Citations: 27
"Milo2.0 unlocks population genetic analyses of cell state abundance using a count-based mixed model"
DOI: 10.1101/2023.11.08.566176, Citations: 5

Platform: R
Code: https://github.com/MarioniLab/miloR
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Marker Genes, Visualisation
Added: 2020-11-29, Updated: 2023-11-17

MarkerMap

MarkerMap is a generative model for selecting the most informative gene markers by projecting cells into a shared, interpretable embedding without sacrificing accuracy

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

Publications
"MarkerMap: nonlinear marker selection for single-cell studies"
DOI: 10.1038/s41540-024-00339-3, Published: 2024-02-14, Citations: 4
Preprints
"MarkerMap: nonlinear marker selection for single-cell studies"
arXiv: 2207.14106, Citations: 0

Platform: Python
Code: https://github.com/Computational-Morphogenomics-Group/MarkerMap
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last commitUnknown
License: MIT
Categories: Marker Genes, Visualisation
Added: 2022-08-19, Updated: 2024-02-23

MetaCell

The MetaCell R package facilitates analysis of single cell RNA-seq UMI matrices by computing partitions of a cell similarity graph into small (~20-200 typically) homogeneous groups of cells which are defined as metacells (MCs).

Publications: 1, Preprints: 1, Total citations: 343

Publications
"MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions"
DOI: 10.1186/s13059-019-1812-2, Published: 2019-10-11, Citations: 331
Preprints
"MetaCell: analysis of single cell RNA-seq data using k-NN graph partitions"
DOI: 10.1101/437665, Citations: 12

Platform: R
Code: https://github.com/tanaylab/metacell
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last commitUnknown
License: MIT
Categories: Clustering, Marker Genes, Quality Control, Variable Genes, Visualisation
Added: 2018-11-13, Updated: 2022-04-01

Monocle

Differential expression and time-series analysis for single-cell RNA-Seq.

Publications: 3, Preprints: 1, Total citations: 12222

Publications
"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells"
DOI: 10.1038/nbt.2859, Published: 2014-03-23, Citations: 6394
"Single-cell mRNA quantification and differential analysis with Census"
DOI: 10.1038/nmeth.4150, Published: 2017-01-23, Citations: 1654
"Reversed graph embedding resolves complex single-cell trajectories"
DOI: 10.1038/nmeth.4402, Published: 2017-08-21, Citations: 4111
Preprints
"Reversed graph embedding resolves complex single-cell developmental trajectories"
DOI: 10.1101/110668, Citations: 63

Platform: R
Code: https://github.com/cole-trapnell-lab/monocle-release
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last commitUnknown
License: Artistic-2.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Expression Patterns, Marker Genes, Ordering, Visualisation
Added: 2016-09-08, Updated: 2018-03-14

muscat

Multi-sample multi-group scRNA-seq analysis tools

Publications: 1, Preprints: 1, Total citations: 410

Publications
"muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data"
DOI: 10.1038/s41467-020-19894-4, Published: 2020-11-30, Citations: 374
Preprints
"On the discovery of subpopulation-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data"
DOI: 10.1101/713412, Citations: 36

Platform: R
Code: https://github.com/HelenaLC/muscat
starsN/A
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last commitUnknown
License: GPL-2.0-or-later
Categories: Differential Expression, Marker Genes, Simulation, Visualisation
Added: 2019-08-05, Updated: 2020-12-12

N

NSForest

Necessary and Sufficient Forest (NS-Forest) for Cell Type Marker Determination from cell type clusters

Publications: 1, Preprints: 1, Total citations: 86

Publications
"A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing"
DOI: 10.1101/gr.275569.121, Published: 2021-06-04, Citations: 78
Preprints
"NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing"
DOI: 10.1101/2020.09.23.308932, Citations: 8

Platform: Python
Code: https://github.com/JCVenterInstitute/NSForest
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last commitUnknown
License: MIT
Categories: Marker Genes
Added: 2021-11-27, Updated: 2023-02-10

O

OCAT

OCAT (One Cell At a Time) provides a fast and memory-efficient framework for analyzing and integrating large-scale scRNA-seq data

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

Publications
"One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data"
DOI: 10.1186/s13059-022-02659-1, Published: 2022-04-20, Citations: 14
Preprints
"One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data"
DOI: 10.1101/2021.05.12.443814, Citations: 2

Platform: Python
Code: https://github.com/bowang-lab/OCAT
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Integration, Marker Genes, Normalisation, Visualisation
Added: 2021-08-06, Updated: 2022-04-30

OmicVerse

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

Publications: 1, Preprints: 1, Total citations: 68

Publications
"OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing"
DOI: 10.1038/s41467-024-50194-3, Published: 2024-07-16, Citations: 59
Preprints
"OmicVerse: A single pipeline for exploring the entire transcriptome universe"
DOI: 10.1101/2023.06.06.543913, Citations: 9

Platform: Python
Code: https://github.com/Starlitnightly/omicverse
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Classification, Gene Networks, Integration, Marker Genes, Ordering, Perturbations
Added: 2023-12-01, Updated: 2025-12-30

OnClass

OnClass is a python package for single-cell cell type annotation. It uses the Cell Ontology to capture the cell type similarity.

Publications: 1, Preprints: 1, Total citations: 65

Publications
"Leveraging the Cell Ontology to classify unseen cell types"
DOI: 10.1038/s41467-021-25725-x, Published: 2021-09-21, Citations: 48
Preprints
"Unifying single-cell annotations based on the Cell Ontology"
DOI: 10.1101/810234, Citations: 17

Platform: Python
Code: https://github.com/wangshenguiuc/OnClass
starsN/A
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last commitUnknown
License: MIT
Categories: Classification, Gene Sets, Integration, Marker Genes
Added: 2019-10-24, Updated: 2021-09-24

Ouija

Incorporate prior information into single-cell trajectory (pseudotime) analyses using Bayesian nonlinear factor analysis.

Publications: 1, Preprints: 1, Total citations: 49

Publications
"A descriptive marker gene approach to single-cell pseudotime inference"
DOI: 10.1093/bioinformatics/bty498, Published: 2018-06-23, Citations: 42
Preprints
"A descriptive marker gene approach to single-cell pseudotime inference"
DOI: 10.1101/060442, Citations: 7

Platform: R
Code: https://github.com/kieranrcampbell/ouija
starsN/A
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last commitUnknown
License: GPL-3.0-or-later
Categories: Expression Patterns, Marker Genes, Ordering, Visualisation
Added: 2016-09-08, Updated: 2018-06-27

P

Piccolo

An R package for processing and analyzing single-cell counts data

Publications: 1, Preprints: 1, Total citations: 3

Publications
"Feature selection followed by a novel residuals-based normalization that includes variance stabilization simplifies and improves single-cell gene expression analysis"
DOI: 10.1186/s12859-024-05872-w, Published: 2024-07-30, Citations: 3
Preprints
"Feature selection followed by a residuals-based normalization simplifies and improves single-cell gene expression analysis"
DOI: 10.1101/2023.03.02.530891, Citations: 0

Platform: R
Code: https://github.com/Amartya101/Piccolo
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Marker Genes, Normalisation, Quality Control, Variable Genes
Added: 2023-03-10, Updated: 2025-04-13

phenotype-cover

Multiset multicover methods for discriminative marker selection

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

Publications
"Multiset multicover methods for discriminative marker selection"
DOI: 10.1016/j.crmeth.2022.100332, Published: 2022-11, Citations: 8

Platform: Python
Code: https://github.com/euxhenh/phenotype-cover
starsN/A
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last commitUnknown
License: MIT
Categories: Marker Genes, Visualisation
Added: 2022-11-25, Updated: 2022-11-25

pyHSICLasso

pyHSICLasso is a package of the Hilbert Schmidt Independence Criterion Lasso (HSIC Lasso), which is a black box (nonlinear) feature selection method considering the nonlinear input and output relationship.

Publications: 1, Preprints: 1, Total citations: 65

Publications
"Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data"
DOI: 10.1093/bioinformatics/btz333, Published: 2019-07-05, Citations: 65
Preprints
"Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data"
DOI: 10.1101/532192, Citations: 0

Platform: Python
Code: https://github.com/riken-aip/pyHSICLasso
starsN/A
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last commitUnknown
License: MIT
Categories: Marker Genes
Added: 2019-02-01, Updated: 2019-02-01

R

RECOMBINE

RECOMBINE (REcurrent COmposite Markers for Biological Identities with Neighborhood Enrichment) is a computational framework for unbiased selection of discriminant markers that hierarchically distinguish cells

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

Preprints
"Recurrent composite markers of cell types and states"
DOI: 10.1101/2023.07.17.549344, Citations: 2

Platform: R/C++
Code: https://github.com/korkutlab/recombine
starsN/A
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last commitUnknown
License: Apache-2.0
Categories: Marker Genes, Visualisation
Added: 2023-07-21, Updated: 2023-07-21

RISC

RISC (Robust Integration of Single Cell RNA-seq) implements the RPCI integration algorithm

Publications: 1, Preprints: 1, Total citations: 45

Publications
"Robust integration of multiple single-cell RNA sequencing datasets using a single reference space"
DOI: 10.1038/s41587-021-00859-x, Published: 2021-03-25, Citations: 43
Preprints
"RISC: robust integration of single-cell RNA-seq datasets with different extents of cell cluster overlap"
DOI: 10.1101/483297, Citations: 2

Platform: R/C++
Code: https://github.com/bioinfoDZ/RISC
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Integration, Marker Genes, Normalisation, Visualisation
Added: 2022-05-27, Updated: 2022-05-27

RZiMM

RZiMM-scRNA, a regularized zero-inflated mixture model designed for sparse single-cell RNA-seq data with sample heterogeneity

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

Preprints
"RZiMM-scRNA: A regularized zero-inflated mixture model framework for single-cell RNA-seq data"
arXiv: 2110.12964, Citations: 0

Platform: R
Code: https://github.com/SkadiEye/RZiMM
starsN/A
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last commitUnknown
License: GPL-2.0-or-later
Categories: Clustering, Marker Genes, Visualisation
Added: 2021-10-29, Updated: 2021-10-29

RaceID

RaceID is a clustering algorithm for the identification of cell types from single-cell RNA-sequencing data. It was specifically designed for the detection of rare cells which correspond to outliers in conventional clustering methods.

Publications: 4, Preprints: 1, Total citations: 1798

Publications
"De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data"
DOI: 10.1016/j.stem.2016.05.010, Published: 2016-08, Citations: 549
"Single-cell messenger RNA sequencing reveals rare intestinal cell types"
DOI: 10.1038/nature14966, Published: 2015-08-19, Citations: 1143
"Revealing dynamics of gene expression variability in cell state space"
DOI: 10.1038/s41592-019-0632-3, Published: 2019-11-18, Citations: 83
"VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells"
DOI: 10.1186/s13059-023-02974-1, Published: 2023-06-23, Citations: 18
Preprints
"Gene expression noise dynamics unveil functional heterogeneity of ageing hematopoietic stem cells"
DOI: 10.1101/2022.08.04.502776, Citations: 5

Platform: R/C++
Code: https://github.com/dgrun/RaceID3_StemID2_package
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License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Expression Patterns, Imputation, Integration, Marker Genes, Ordering, Rare Cells, Stem Cells, Visualisation
Added: 2016-09-09, Updated: 2023-07-14

rCASC

rCASC, Cluster Analysis of Single Cells

Publications: 2, Preprints: 2, Total citations: 66

Publications
"Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining"
DOI: 10.1038/s41540-020-00162-6, Published: 2021-01-05, Citations: 37
"rCASC: reproducible classification analysis of single-cell sequencing data"
DOI: 10.1093/gigascience/giz105, Published: 2019-09-08, Citations: 28
Preprints
"Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining"
DOI: 10.1101/2020.05.26.117705, Citations: 0
"rCASC: reproducible Classification Analysis of Single Cell sequencing data"
DOI: 10.1101/430967, Citations: 1

Platform: R/Docker
Code: https://github.com/kendomaniac/rCASC
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License: GPL-3.0
Categories: Alignment, Cell Cycle, Clustering, Dimensionality Reduction, Gene Filtering, Marker Genes, Normalisation, Quality Control, Quantification, UMIs, Visualisation
Added: 2018-11-14, Updated: 2020-06-05

redPATH

redPATH formulates the problem of pseudo temporal ordering into a Hamiltonian path problem and attempts to recover the pseudo development time in single-cell RNA-seq datasets

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

Publications
"redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer"
DOI: 10.1016/j.gpb.2020.06.014, Published: 2021-02, Citations: 4
Preprints
"redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer"
DOI: 10.1101/2020.03.05.977686, Citations: 1

Platform: R/C++
Code: https://github.com/tinglab/redPATH
starsN/A
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last commitUnknown
Categories: Clustering, Gene Sets, Marker Genes, Ordering, Variable Genes, Visualisation
Added: 2020-03-18, Updated: 2020-03-18

RankCorr

A marker selection method for scRNA-seq data based on rank correlation

Publications: 1, Preprints: 0, Total citations: 44

Publications
"A rank-based marker selection method for high throughput scRNA-seq data"
DOI: 10.1186/s12859-020-03641-z, Published: 2020-10-23, Citations: 44

Platform: Python
Code: https://github.com/ahsv/RankCorr
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last commitUnknown
Categories: Marker Genes
Added: 2020-11-08, Updated: 2020-11-08

S

SAM

The self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction.

Publications: 1, Preprints: 1, Total citations: 80

Publications
"Self-assembling manifolds in single-cell RNA sequencing data"
DOI: 10.7554/eLife.48994, Published: 2019-09-16, Citations: 77
Preprints
"Self-assembling Manifolds in Single-cell RNA Sequencing Data"
DOI: 10.1101/364166, Citations: 3

Platform: Python
Code: https://github.com/atarashansky/self-assembling-manifold
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License: MIT
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Interactive, Marker Genes, Normalisation, Visualisation
Added: 2019-11-03, Updated: 2021-06-28

SCALA

A web application for multimodal analysis of single cell next generation sequencing data

Publications: 1, Preprints: 1, Total citations: 6

Publications
"SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data"
DOI: 10.1016/j.csbj.2023.10.032, Published: 2023, Citations: 5
Preprints
"SCALA: A web application for multimodal analysis of single cell next generation sequencing data"
DOI: 10.1101/2022.11.24.517826, Citations: 1

Platform: R/JavaScript
Code: https://github.com/PavlopoulosLab/SCALA
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License: GPL-3.0
Categories: Cell Cycle, Classification, Clustering, Differential Expression, Dimensionality Reduction, Expression Patterns, Gene Filtering, Gene Networks, Integration, Interactive, Marker Genes, Normalisation, Ordering, Quality Control, Variable Genes, Visualisation
Added: 2023-11-17, Updated: 2023-11-17

SCISSORS

This package allows the reclustering of single cell clusters based on the identification of the highly variable genes that are unique to each cluster

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

Publications
"Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in Single-Cell RNA-Sequencing"
DOI: 10.1093/bioinformatics/btad449, Published: 2023-07-27, Citations: 15
Preprints
"Sub-Cluster Identification through Semi-Supervised Optimization of Rare-cell Silhouettes (SCISSORS) in Single-Cell Sequencing"
DOI: 10.1101/2021.10.29.466448, Citations: 1

Platform: R
Code: https://github.com/jr-leary7/SCISSORS
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License: MIT
Categories: Clustering, Dimensionality Reduction, Marker Genes
Added: 2021-11-05, Updated: 2023-08-04

SEGtool

SEGtool is an R package with self-adaptive function and high accuracy for specifically expressed gene (SEG, also known as tissue specific gene) detection.

Publications: 1, Preprints: 0, Total citations: 20

Publications
"SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data"
DOI: 10.1093/bib/bbx074, Published: 2017-07-07, Citations: 20

Platform: R
Code: http://bioinfo.life.hust.edu.cn/SEGtool/
Categories: Gene Filtering, Marker Genes
Added: 2017-07-16, Updated: 2017-07-16

SEMITONES

Single-cEll Marker IdentificaTiON by Enrichment Scoring

Publications: 1, Preprints: 1, Total citations: 13

Publications
"Cluster-independent marker feature identification from single-cell omics data using SEMITONES"
DOI: 10.1093/nar/gkac639, Published: 2022-07-31, Citations: 11
Preprints
"SEMITONES: Single-cEll Marker IdentificaTiON by Enrichment Scoring"
DOI: 10.1101/2020.11.17.386664, Citations: 2

Platform: Python
Code: https://github.com/ohlerlab/SEMITONES
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License: GPL-3.0
Categories: Gene Sets, Marker Genes, Visualisation
Added: 2020-11-22, Updated: 2022-08-19

SHARP

Single-cell RNA-seq Hyper-fast and Accurate processing via ensemble Random Projection

Publications: 1, Preprints: 1, Total citations: 86

Publications
"SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection"
DOI: 10.1101/gr.254557.119, Published: 2020-01-28, Citations: 84
Preprints
"SHARP: Single-cell RNA-seq Hyper-fast and Accurate Processing via Ensemble Random Projection"
DOI: 10.1101/461640, Citations: 2

Platform: R
Code: https://github.com/shibiaowan/SHARP
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License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Marker Genes, Normalisation, Visualisation
Added: 2018-11-09, Updated: 2020-02-13

SINCERA

R-based pipeline for single-cell analysis including clustering and visualization.

Publications: 2, Preprints: 0, Total citations: 345

Publications
"Single-Cell Transcriptome Analysis Using SINCERA Pipeline"
DOI: 10.1007/978-1-4939-7710-9_15, Published: 2018-03-06, Citations: 14
"SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis"
DOI: 10.1371/journal.pcbi.1004575, Published: 2015-11-24, Citations: 331

Platform: R
Code: https://github.com/xu-lab/SINCERA
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License: GPL-3.0
Categories: Clustering, Differential Expression, Gene Filtering, Marker Genes, Normalisation, Quality Control
Added: 2016-09-08, Updated: 2018-03-14

SMaSH

The SMaSH (Scalable Marker gene Signal Hunter) framework is a general, scalable codebase for calculating marker genes from single-cell RNA-sequencing

Publications: 1, Preprints: 1, Total citations: 35

Publications
"SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing"
DOI: 10.1186/s12859-022-04860-2, Published: 2022-08-08, Citations: 27
Preprints
"SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics"
DOI: 10.1101/2021.04.08.438978, Citations: 8

Platform: Python
Code: https://gitlab.com/cvejic-group/smash
License: MIT
Categories: Gene Filtering, Marker Genes
Added: 2023-06-02, Updated: 2023-06-02

SPADE

Visualization and cellular hierarchy inference of single-cell data using SPADE.

Publications: 2, Preprints: 0, Total citations: 979

Publications
"Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE"
DOI: 10.1038/nbt.1991, Published: 2011-10-02, Citations: 878
"Visualization and cellular hierarchy inference of single-cell data using SPADE"
DOI: 10.1038/nprot.2016.066, Published: 2016-06-16, Citations: 101

Platform: R
Code: https://github.com/nolanlab/spade
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License: GPL-2.0-or-later
Categories: Clustering, Dimensionality Reduction, Marker Genes, Ordering, Visualisation
Added: 2016-09-09, Updated: 2018-03-07

SPECTRA

Supervised Pathway DEConvolution of InTerpretable Gene ProgRAms

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

Publications
"Supervised discovery of interpretable gene programs from single-cell data"
DOI: 10.1038/s41587-023-01940-3, Published: 2023-09-21, Citations: 63
Preprints
"Supervised discovery of interpretable gene programs from single-cell data"
DOI: 10.1101/2022.12.20.521311, Citations: 8

Platform: Python
Code: https://github.com/dpeerlab/spectra
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License: MIT
Categories: Gene Networks, Gene Sets, Marker Genes, Visualisation
Added: 2023-01-04, Updated: 2024-01-05

SPmarker

SPmarker is a machine learning based approach for identification of marker genes and classification of cells in plant tissues

Publications: 1, Preprints: 1, Total citations: 31

Publications
"Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods"
DOI: 10.1111/nph.18053, Published: 2022-02-24, Citations: 26
Preprints
"Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods"
DOI: 10.1101/2020.11.22.393165, Citations: 5

Platform: Python
Code: https://github.com/LiLabAtVT/SPMarker
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License: GPL-3.0
Categories: Marker Genes
Added: 2021-10-18, Updated: 2022-03-04

SVP

SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets
Platform: R/C++
Code: https://github.com/YuLab-SMU/SVP
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License: GPL-3.0
Categories: Dimensionality Reduction, Gene Sets, Marker Genes, Visualisation
Added: 2025-04-24, Updated: 2025-04-24

Sargent

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: 2, Preprints: 0, Total citations: 14

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: 14
"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

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: 4

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: 4
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
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Categories: Clustering, Dimensionality Reduction, Gene Networks, Gene Sets, Imputation, Marker Genes, Normalisation, Perturbations, Quality Control
Added: 2023-10-27, Updated: 2024-05-06

sc2marker

sc2marker is a toolkit for identification of cell specific markers for clusters of cells in a single cell RNA sequencing data set

Publications: 1, Preprints: 0, Total citations: 23

Publications
"Detection of cell markers from single cell RNA-seq with sc2marker"
DOI: 10.1186/s12859-022-04817-5, Published: 2022-07-12, Citations: 23

Platform: R
Code: https://github.com/CostaLab/sc2marker
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License: MIT
Categories: Marker Genes, Normalisation, Visualisation
Added: 2022-08-12, Updated: 2022-08-12

scAAnet

Decompose an expression profile into a usage matrix and a GEP/archetype matrix

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

Publications
"Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders"
DOI: 10.1371/journal.pcbi.1010025, Published: 2022-04-01, Citations: 16
Preprints
"Non-linear Archetypal Analysis of Single-cell RNA-seq Data by Deep Autoencoders"
DOI: 10.1101/2021.09.17.460824, Citations: 0

Platform: Python
Code: https://github.com/AprilYuge/scAAnet_latest
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License: MIT
Categories: Clustering, Marker Genes, Visualisation
Added: 2021-09-24, Updated: 2022-04-30

scAI

scAI: a single cell Aggregation and Integration method for analyzing single cell multi-omics data

Publications: 1, Preprints: 0, Total citations: 147

Publications
"scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles"
DOI: 10.1186/s13059-020-1932-8, Published: 2020-02-03, Citations: 147

Platform: R/MATLAB
Code: https://github.com/sqjin/scAI
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Integration, Marker Genes, Visualisation
Added: 2020-02-20, Updated: 2020-02-20

scAIDE

scAIDE is an unsupervised clustering framework for single-cell RNA-seq data

Publications: 1, Preprints: 0, Total citations: 32

Publications
"scAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types"
DOI: 10.1093/nargab/lqaa082, Published: 2020-10-09, Citations: 32

Platform: Python/R
Code: https://github.com/tinglabs/scAIDE
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Categories: Clustering, Marker Genes, Visualisation
Added: 2020-10-27, Updated: 2020-10-27

scASfind

scASfind conducts rapid searching and returns feature-enriched cell types or cell type-specific splicing signatures, respectively

Publications: 1, Preprints: 1, Total citations: 6

Publications
"Mining alternative splicing patterns in scRNA-seq data using scASfind"
DOI: 10.1186/s13059-024-03323-6, Published: 2024-07-29, Citations: 5
Preprints
"Mining alternative splicing patterns in scRNA-seq data using scASfind"
DOI: 10.1101/2023.08.19.553947, Citations: 1

Platform: R/C++
Code: https://github.com/hemberg-lab/scASfind
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last commitUnknown
License: MIT
Categories: Alternative Splicing, Marker Genes, Visualisation
Added: 2023-08-24, Updated: 2025-04-13

scAnCluster

A deep leanring based clustering and annotation algorithm for scRNASeq data

Publications: 1, Preprints: 0, Total citations: 35

Publications
"Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation"
DOI: 10.3390/genes11070792, Published: 2020-07-14, Citations: 35

Platform: Python
Code: https://github.com/xuebaliang/scAnCluster
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Categories: Classification, Clustering, Marker Genes
Added: 2020-08-06, Updated: 2020-08-06

scAnno

scAnno is an automated annotation tool for single-cell RNA sequencing datasets primarily based on the single cell cluster level

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

Publications
"scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets"
DOI: 10.1093/bib/bbad179, Published: 2023-05-14, Citations: 11

Platform: R
Code: https://github.com/liuhong-jia/scAnno
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last commitUnknown
License: GPL-3.0
Categories: Classification, Marker Genes, Variable Genes
Added: 2023-06-02, Updated: 2023-06-02

scAnnoX

scAnnoX is an R package that integrates 10 different cell identity algorithms for single-cell sequencing data into a unified framework

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

Publications
"scAnnoX: an R package integrating multiple public tools for single-cell annotation"
DOI: 10.7717/peerj.17184, Published: 2024-03-28, Citations: 4

Platform: R/C++
Code: https://github.com/XQ-hub/scAnnoX
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last commitUnknown
License: GPL-3.0-or-later
Categories: Classification, Marker Genes
Added: 2024-04-05, Updated: 2024-04-05

scAnnotatR

The scAnnotatR package automatically classifies cells in scRNA-seq datasets

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

Publications
"scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data"
DOI: 10.1186/s12859-022-04574-5, Published: 2022-01-17, Citations: 24
Preprints
"scClassifR: Framework to accurately classify cell types in single-cell RNA-sequencing data"
DOI: 10.1101/2020.12.22.424025, Citations: 2

Platform: R
Code: https://github.com/grisslab/scAnnotatR
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License: MIT
Categories: Classification, Marker Genes, Visualisation
Added: 2021-11-05, Updated: 2022-02-04

scCATCH

Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

Publications: 1, Preprints: 0, Total citations: 268

Publications
"scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data"
DOI: 10.1016/j.isci.2020.100882, Published: 2020-03, Citations: 268

Platform: R
Code: https://github.com/ZJUFanLab/scCATCH
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License: GPL-3.0
Categories: Classification, Marker Genes
Added: 2020-04-29, Updated: 2022-01-14

scCapsNet-mask

scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data

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

Publications
"scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data"
DOI: 10.1186/s12859-022-05098-8, Published: 2022-12-12, Citations: 4
Preprints
"scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data"
DOI: 10.21203/rs.3.rs-1763879/v1, Citations: 0

Platform: Python
Code: https://github.com/wanglf19/scCapsNet_mask
starsN/A
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last commitUnknown
Categories: Classification, Marker Genes, Visualisation
Added: 2022-09-02, Updated: 2023-01-04

scDEF

scDEF learns hierarchies of cell states and their gene signatures from scRNA-seq data

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

Preprints
"Deep exponential families for single-cell data analysis"
DOI: 10.1101/2022.10.15.512383, Citations: 2

Platform: Python
Code: https://github.com/cbg-ethz/scDEF
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last commitUnknown
Categories: Marker Genes, Visualisation
Added: 2024-01-05, Updated: 2024-01-05

scDataviz

scDataviz: single cell dataviz and downstream analyses
Platform: R
Code: https://github.com/kevinblighe/scDataviz
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Gene Sets, Marker Genes, Visualisation
Added: 2020-02-21, Updated: 2020-10-30

scFlow

The scFlow R package is built to enable standardized workflows following best practices on top of popular single-cell R packages, including Seurat, Monocle, scater, emptyDrops, DoubletFinder, LIGER, and MAST

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

Preprints
"scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data"
DOI: 10.1101/2021.08.16.456499, Citations: 19

Platform: R
Code: https://github.com/combiz/scflow
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last commitUnknown
License: GPL-3.0
Categories: Classification, Clustering, Dimensionality Reduction, Gene Sets, Integration, Marker Genes, Quality Control, Visualisation
Added: 2021-09-03, Updated: 2021-09-03

scGEAToolbox

scGEAToolbox is a Matlab toolbox for scRNA-seq data analysis

Publications: 1, Preprints: 1, Total citations: 29

Publications
"scGEAToolbox: a Matlab toolbox for single-cell RNA sequencing data analysis"
DOI: 10.1093/bioinformatics/btz830, Published: 2019-11-07, Citations: 25
Preprints
"scGEApp: a Matlab app for feature selection on single-cell RNA sequencing data"
DOI: 10.1101/544163, Citations: 4

Platform: MATLAB
Code: https://github.com/jamesjcai/scGEAToolbox
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last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Gene Networks, Gene Sets, Integration, Marker Genes, Normalisation, Ordering, Quality Control, Visualisation
Added: 2019-02-25, Updated: 2021-06-28

scGPS

A complete single cell RNA analysis framework to decompose a mixed population into clusters (SCORE) and to analyse the relationship between clusters (scGPS).
Platform: R/C++
Code: https://github.com/IMB-Computational-Genomics-Lab/scGPS
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License: GPL-3.0
Categories: Clustering, Marker Genes, Variable Genes, Visualisation
Added: 2018-07-18, Updated: 2019-11-03

scGREAT

Single-cell Graph-based Regulatory Element Analysis Toolkit (scGREAT) takes as input the single-cell multiome data for various kinds of regulation-based analyses

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

Preprints
"scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data"
DOI: 10.1101/2023.01.27.525916, Citations: 1

Platform: Python
Code: https://github.com/ChaozhongLiu/scGREAT
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Gene Networks, Marker Genes, Ordering, Visualisation
Added: 2023-02-03, Updated: 2023-02-03

scGeneFit

cGeneFit, selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods

Publications: 1, Preprints: 1, Total citations: 89

Publications
"Optimal marker gene selection for cell type discrimination in single cell analyses"
DOI: 10.1038/s41467-021-21453-4, Published: 2021-02-19, Citations: 83
Preprints
"Optimal marker gene selection for cell type discrimination in single cell analyses"
DOI: 10.1101/599654, Citations: 6

Platform: MATLAB
Code: https://github.com/solevillar/scGeneFit
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last commitUnknown
Categories: Marker Genes
Added: 2019-04-05, Updated: 2019-04-05

scID

scID is a method for geneset-guided identification of cell types at the level of individual cells, using single cell RNA-seq data.

Publications: 1, Preprints: 1, Total citations: 77

Publications
"scID uses discriminant analysis to identify transcriptionally equivalent cell types across single cell RNA-seq data with batch effect"
DOI: 10.1016/j.isci.2020.100914, Published: 2020-03, Citations: 66
Preprints
"scID: Identification of equivalent transcriptional cell populations across single cell RNA-seq data using discriminant analysis"
DOI: 10.1101/470203, Citations: 11

Platform: R
Code: https://github.com/BatadaLab/scID
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last commitUnknown
License: GPL-3.0
Categories: Classification, Gene Sets, Marker Genes, Normalisation, Visualisation
Added: 2018-11-16, Updated: 2020-02-21

scIVA

scIVA - Single Cell Interactive Visualisation and Analysis
Platform: R
Code: https://github.com/IMB-Computational-Genomics-Lab/scIVA
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last commitUnknown
Categories: Gene Sets, Interactive, Marker Genes, Quality Control, Visualisation
Added: 2018-12-13, Updated: 2018-12-13

scMAGS

scmags selects marker genes from scRNA-seq data that are exclusive to each cell type such that, selected marker genes are highly expressed in a specific cell type while lowly expressed in other cell types

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

Publications
"scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies"
DOI: 10.1016/j.compbiomed.2023.106634, Published: 2023-03, Citations: 10
Preprints
"scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies"
DOI: 10.1101/2022.03.22.485261, Citations: 0

Platform: Python
Code: https://github.com/doganlab/scmags
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last commitUnknown
License: Apache-2.0
Categories: Dimensionality Reduction, Gene Filtering, Marker Genes, Visualisation
Added: 2022-03-25, Updated: 2023-05-12

scMC

scMC is an R toolkit for integrating and comparing multiple single cell genomic datasets from single cell RNA-seq and ATAC-seq experiments across different conditions, time points and tissues

Publications: 1, Preprints: 0, Total citations: 807

Publications
"scMC learns biological variation through the alignment of multiple single-cell genomics datasets"
DOI: 10.1186/s13059-020-02238-2, Published: 2021-01-04, Citations: 807

Platform: R
Code: https://github.com/amsszlh/scMC
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License: GPL-3.0
Categories: Clustering, Integration, Marker Genes, Visualisation
Added: 2021-01-15, Updated: 2021-01-15

Scedar

Scedar (Single-cell exploratory data analysis for RNA-Seq) is a reliable and easy-to-use Python package for efficient visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering of large-scale single cell RNA-seq (scRNA-seq) datasets.

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

Publications
"Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis"
DOI: 10.1371/journal.pcbi.1007794, Published: 2020-04-27, Citations: 15
Preprints
"Scedar: a scalable Python package for single-cell RNA-seq exploratory data analysis"
DOI: 10.1101/375196, Citations: 1

Platform: Python
Code: https://github.com/TaylorResearchLab/scedar
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License: MIT
Categories: Clustering, Dimensionality Reduction, Imputation, Marker Genes, Rare Cells, Visualisation
Added: 2018-11-13, Updated: 2020-09-24

Seurat

It contains easy-to-use implementations of commonly used analytical techniques, including the identification of highly variable genes, dimensionality reduction (PCA, ICA, t-SNE), standard unsupervised clustering algorithms (density clustering, hierarchical clustering, k-means), and the discovery of differentially expressed genes and markers.

Publications: 6, Preprints: 5, Total citations: 52541

Publications
"Comprehensive Integration of Single-Cell Data"
DOI: 10.1016/j.cell.2019.05.031, Published: 2019-06, Citations: 13838
"Integrated analysis of multimodal single-cell data"
DOI: 10.1016/j.cell.2021.04.048, Published: 2021-06, Citations: 12800
"Spatial reconstruction of single-cell gene expression data"
DOI: 10.1038/nbt.3192, Published: 2015-04-13, Citations: 6231
"Integrating single-cell transcriptomic data across different conditions, technologies, and species"
DOI: 10.1038/nbt.4096, Published: 2018-04-02, Citations: 11865
"Dictionary learning for integrative, multimodal and scalable single-cell analysis"
DOI: 10.1038/s41587-023-01767-y, Published: 2023-05-25, Citations: 3347
"Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression"
DOI: 10.1186/s13059-019-1874-1, Published: 2019-12-23, Citations: 3888
Preprints
"Integrated analysis of single cell transcriptomic data across conditions, technologies, and species"
DOI: 10.1101/164889, Citations: 55
"Integrated analysis of multimodal single-cell data"
DOI: 10.1101/2020.10.12.335331, Citations: 191
"Dictionary learning for integrative, multimodal, and scalable single-cell analysis"
DOI: 10.1101/2022.02.24.481684, Citations: 86
"Comprehensive integration of single cell data"
DOI: 10.1101/460147, Citations: 137
"Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression"
DOI: 10.1101/576827, Citations: 103

Platform: R
Code: https://github.com/satijalab/seurat
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License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Imputation, Integration, Marker Genes, Normalisation, Variable Genes, Visualisation
Added: 2016-09-08, Updated: 2024-01-05

SeuratV3Wizard

R Shiny interface for Seurat v3 single-cell analysis library
Platform: R
Code: https://github.com/nasqar/seuratv3wizard
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Interactive, Marker Genes, Normalisation, Quality Control, Variable Genes
Added: 2019-03-01, Updated: 2019-03-01

SeuratWizard

R Shiny interface for Seurat single-cell analysis library
Platform: R
Code: https://github.com/nasqar/SeuratWizard
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Interactive, Marker Genes, Normalisation, Quality Control, Variable Genes
Added: 2019-01-18, Updated: 2021-06-28

SignacX

Signac helps solve the cell type classification problem in single cell RNA sequencing

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

Publications
"Cell Type Classification and Discovery across Diseases, Technologies and Tissues Reveals Conserved Gene Signatures of Immune Phenotypes"
DOI: 10.26502/jbsb.5107057, Published: 2023, Citations: 3
Preprints
"Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts"
DOI: 10.1101/2021.02.01.429207, Citations: 8

Platform: R
Code: https://github.com/mathewchamberlain/SignacX
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last commitUnknown
License: GPL-3.0
Categories: Classification, Immune, Marker Genes
Added: 2021-02-05, Updated: 2024-01-05

SingCellaR

An integrative analysis tool for analysing single-cell RNA sequencing data

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

Publications
"Processing single-cell RNA-seq datasets using SingCellaR"
DOI: 10.1016/j.xpro.2022.101266, Published: 2022-06, Citations: 11

Platform: R/C++
Code: https://github.com/supatt-lab/SingCellaR
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License: GPL-3.0
Categories: Cell Cycle, Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Sets, Integration, Marker Genes, Quality Control, Visualisation
Added: 2022-04-08, Updated: 2022-04-08

SingleCellSignalR

Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions.

Publications: 1, Preprints: 1, Total citations: 381

Publications
"SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics"
DOI: 10.1093/nar/gkaa183, Published: 2020-03-20, Citations: 375
Preprints
"SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics"
DOI: 10.1101/2019.12.11.872895, Citations: 6

Platform: R
Code: https://github.com/SCA-IRCM/SingleCellSignalR
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License: GPL-3.0
Categories: Classification, Clustering, Gene Networks, Marker Genes, Normalisation, Visualisation
Added: 2019-12-18, Updated: 2020-04-29

SparseDC

A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

Publications: 1, Preprints: 0, Total citations: 15

Publications
"A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data"
DOI: 10.1093/nar/gkx1113, Published: 2017-11-13, Citations: 15

Platform: R
Code: https://github.com/cran/SparseDC
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License: GPL-3.0
Categories: Clustering, Marker Genes
Added: 2017-11-21, Updated: 2017-11-21

Spectre

Spectre is an R package and computational toolkit that enables comprehensive end-to-end integration, exploration, and analysis of high-dimensional cytometry, imaging, or single-cell data from different batches or experiments

Publications: 1, Preprints: 1, Total citations: 155

Publications
"Integration, exploration, and analysis of high‐dimensional single‐cell cytometry data using Spectre"
DOI: 10.1002/cyto.a.24350, Published: 2021-04-26, Citations: 146
Preprints
"Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre"
DOI: 10.1101/2020.10.22.349563, Citations: 9

Platform: R
Code: https://github.com/ImmuneDynamics/Spectre
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License: MIT
Categories: Classification, Clustering, Differential Expression, Dimensionality Reduction, Expression Patterns, Immune, Integration, Marker Genes, Normalisation, Ordering, Quality Control, Rare Cells, Stem Cells
Added: 2021-11-05, Updated: 2021-11-05

SuperCell

SuperCell is a network-based coarse-graining framework where highly similar cells are merged into super-cells

Publications: 1, Preprints: 1, Total citations: 52

Publications
"Metacells untangle large and complex single-cell transcriptome networks"
DOI: 10.1186/s12859-022-04861-1, Published: 2022-08-13, Citations: 50
Preprints
"Metacells untangle large and complex single-cell transcriptome networks"
DOI: 10.1101/2021.06.07.447430, Citations: 2

Platform: R
Code: https://github.com/GfellerLab/SuperCell
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License: GPL-3.0
Categories: Clustering, Marker Genes, Ordering, Visualisation
Added: 2021-06-11, Updated: 2024-09-15

scMINER

scMINER is an R package for preprocessing, QC, clustering, and hidden driver analysis of single-cell RNA-seq data

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

Preprints
"scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data"
DOI: 10.1101/2023.01.26.523391, Citations: 4

Platform: R
Code: https://github.com/jyyulab/scMINER
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last commitUnknown
License: Apache-2.0
Categories: Differential Expression, Gene Networks, Marker Genes, Quality Control, Visualisation
Added: 2023-02-03, Updated: 2023-02-03

scMiko

scMiko is an R package developed for single-cell RNA-seq analysis. It was designed using the Seurat framework, and offers existing and novel single-cell analytic work flows.

Publications: 1, Preprints: 2, Total citations: 15

Publications
"Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline"
DOI: 10.1038/s42003-022-04093-2, Published: 2022-10-28, Citations: 14
Preprints
"Multi-level cellular and functional annotation of single-cell transcriptomes"
DOI: 10.1101/2022.03.13.484162, Citations: 1
"Multi-level cellular and functional annotation of single-cell transcriptomes"
DOI: 10.21203/rs.3.rs-1555516/v1, Citations: 0

Platform: R
Code: https://github.com/NMikolajewicz/scMiko
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License: MIT
Categories: Classification, Clustering, Differential Expression, Dimensionality Reduction, Gene Sets, Integration, Marker Genes, Normalisation, Visualisation
Added: 2022-03-18, Updated: 2022-11-04

scRNASequest

An ecosystem of scRNA-seq analysis, visualization and publishing

Publications: 1, Preprints: 0, Total citations: 6

Publications
"scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing"
DOI: 10.1186/s12864-023-09332-2, Published: 2023-05-02, Citations: 6

Platform: R/Python/Shell
Code: https://github.com/interactivereport/scRNASequest
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last commitUnknown
Categories: Classification, Differential Expression, Dimensionality Reduction, Integration, Marker Genes, Quality Control, Visualisation
Added: 2023-05-26, Updated: 2023-05-26

scRNAbox

ScRNAbox is a single-cell RNA sequencing (scRNAseq) pipeline specifically designed for analyzing data under a High-Performance Computing (HPC) systems using the Slurm Workload Manager

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

Publications
"ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems"
DOI: 10.1186/s12859-024-05935-y, Published: 2024-10-01, Citations: 2
Preprints
"ScRNAbox: Empowering Single-Cell RNA Sequencing on High Performance Computing Systems"
DOI: 10.1101/2023.11.13.566851, Citations: 3

Platform: R/JavaScript/Shell
Code: https://github.com/neurobioinfo/scrnabox
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last commitUnknown
License: MIT
Categories: Alignment, Clustering, Differential Expression, Gene Sets, Integration, Marker Genes, Quality Control, Quantification, Visualisation
Added: 2023-11-17, Updated: 2025-04-13

scSeqR

scSeqR (Single Cell Sequencing R package) is an interactive R package to works with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq and CITE-seq).
Platform: R
Code: https://github.com/rezakj/scSeqR
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Sets, Integration, Interactive, Marker Genes, Normalisation, Quality Control, Visualisation
Added: 2018-11-09, Updated: 2018-11-09

scSniper

A novel method for biomarker discovery that leverages deep neural network sensitivity analysis and an attention mechanism to select pivotal multi-omics biomarkers without relying on differential expression

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

Preprints
"scSniper: Single-cell Deep Neural Network-based Identification of Prominent Biomarkers"
DOI: 10.1101/2023.11.22.568389, Citations: 0

Platform: Python
Code: https://github.com/mcgilldinglab/scSniper
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last commitUnknown
License: MIT
Categories: Marker Genes
Added: 2023-12-01, Updated: 2023-12-01

scTIM

Seeking Cell-Type-Indicative Marker from single cell RNA-seq data by consensus optimization

Publications: 1, Preprints: 0, Total citations: 15

Publications
"scTIM: Seeking Cell-Type-Indicative Marker from single cell RNA-seq data by consensus optimization"
DOI: 10.1093/bioinformatics/btz936, Published: 2019-12-17, Citations: 15

Platform: Python
Code: https://github.com/Frank-Orwell/scTIM
starsN/A
forksN/A
last commitUnknown
Categories: Marker Genes
Added: 2020-01-02, Updated: 2020-01-02

scTITANS

Identifying key genes and cell subclusters for time-series single cell sequencing data

Publications: 1, Preprints: 0, Total citations: 7

Publications
"Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS"
DOI: 10.1016/j.csbj.2021.07.016, Published: 2021, Citations: 7

Platform: R
Code: https://github.com/ZJUFanLab/scTITANS
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Marker Genes
Added: 2021-07-31, Updated: 2021-07-31

scTree

An R package to generate antibody-compatible classifiers from singel-cell sequencing data

Publications: 1, Preprints: 0, Total citations: 3

Publications
"scTree: An R package to generate antibody-compatible classifiers from single-cell sequencing data"
DOI: 10.21105/joss.02061, Published: 2020-04-26, Citations: 3

Platform: R
Code: https://github.com/jspaezp/sctree
starsN/A
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last commitUnknown
License: Apache-2.0
Categories: Classification, Marker Genes, Visualisation
Added: 2020-05-05, Updated: 2020-05-05

scX

scX is an R package that enables interactive visualization and analysis of single-cell experiment data

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

Preprints
"scX: A user-friendly tool for scRNA-seq exploration"
arXiv: 2311.00012, Citations: 0

Platform: R
Code: https://github.com/chernolabs/scX
starsN/A
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last commitUnknown
License: MIT
Categories: Differential Expression, Interactive, Marker Genes, Visualisation
Added: 2023-11-10, Updated: 2023-11-10

scaLR

scaLR is a comprehensive end-to-end pipeline that is equipped with a range of advanced features to streamline and enhance the analysis of scRNA-seq data

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

Publications
"scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery"
DOI: 10.1093/bib/bbaf243, Published: 2025-05-01, Citations: 0
Preprints
"scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery"
DOI: 10.1101/2024.09.19.613226, Citations: 0

Platform: Python
Code: https://github.com/infocusp/scaLR
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Classification, Differential Expression, Gene Filtering, Marker Genes, Normalisation, Visualisation
Added: 2024-11-20, Updated: 2025-12-30

scarf

Scarf is a Python package to performs memory efficient analysis of single-cell genomics data

Publications: 1, Preprints: 1, Total citations: 32

Publications
"Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data"
DOI: 10.1038/s41467-022-32097-3, Published: 2022-08-08, Citations: 31
Preprints
"Scarf: A toolkit for memory efficient analysis of large-scale single-cell genomics data"
DOI: 10.1101/2021.05.02.441899, Citations: 1

Platform: Python
Code: https://github.com/parashardhapola/scarf
starsN/A
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last commitUnknown
License: BSD-3-Clause
Categories: Cell Cycle, Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Imputation, Marker Genes, Normalisation, Ordering, Quality Control, Variable Genes, Visualisation
Added: 2021-05-07, Updated: 2022-08-26

scdrake

{scdrake} is a scalable and reproducible pipeline for secondary analysis of droplet-based single-cell RNA-seq data

Publications: 1, Preprints: 0, Total citations: 3

Publications
"Scdrake: a reproducible and scalable pipeline for scRNA-seq data analysis"
DOI: 10.1093/bioadv/vbad089, Published: 2023-07-06, Citations: 3

Platform: R
Code: https://github.com/bioinfocz/scdrake
starsN/A
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last commitUnknown
License: MIT
Categories: Cell Cycle, Classification, Clustering, Dimensionality Reduction, Integration, Marker Genes, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2023-07-14, Updated: 2023-07-14

scfind

scfind - Fast searches of large collections of single cell data

Publications: 1, Preprints: 1, Total citations: 12

Publications
"Fast searches of large collections of single-cell data using scfind"
DOI: 10.1038/s41592-021-01076-9, Published: 2021-03-01, Citations: 10
Preprints
"Fast searches of large collections of single cell data using scfind"
DOI: 10.1101/788596, Citations: 2

Platform: R/C++
Code: https://github.com/hemberg-lab/scfind
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Classification, Interactive, Marker Genes
Added: 2019-10-09, Updated: 2021-06-28

scicast

Single Cell Iterative Clustering and Significance Testing
Platform: Python
Code: https://github.com/iandriver/SCICAST
starsN/A
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last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Interactive, Marker Genes, Normalisation, Visualisation
Added: 2018-11-13, Updated: 2018-11-13

scmixture

A Bayesian mixture model approach to zero-inflation of counts for scRNA-seq data

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

Publications
"Identifying Subpopulations of Cells in Single-Cell Transcriptomic Data: A Bayesian Mixture Modeling Approach to Zero Inflation of Counts"
DOI: 10.1089/cmb.2022.0273, Published: 2023-10-01, Citations: 0
Preprints
"Identifying sub-populations of cells in single cell transcriptomic data - a Bayesian mixture model approach to zero-inflation of counts"
DOI: 10.1101/2021.05.19.444841, Citations: 0

Platform: Julia/R/Python
Code: https://github.com/tt104/scmixture
starsN/A
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last commitUnknown
Categories: Clustering, Dimensionality Reduction, Gene Sets, Marker Genes, Visualisation
Added: 2021-07-31, Updated: 2024-01-05

signac

A versatile R package to facilitate the analysis workflow for single-cell data

Publications: 0, Preprints: 1, Total citations: 18

Preprints
"Venice: A New Algorithm for Finding Marker Genes in Single-Cell Transcriptomic Data"
DOI: 10.1101/2020.11.16.384479, Citations: 18

Platform: R/C++
Code: https://github.com/bioturing/signac
starsN/A
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last commitUnknown
License: Custom
Categories: Differential Expression, Expression Patterns, Marker Genes
Added: 2019-07-11, Updated: 2020-11-22

singleCellHaystack

singleCellHaystack is a package for finding surprising needles (=genes) in haystacks (=multi-dimensional representations of single cell transcriptome data)

Publications: 2, Preprints: 2, Total citations: 92

Publications
"A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data"
DOI: 10.1038/s41467-020-17900-3, Published: 2020-08-28, Citations: 84
"A universal tool for predicting differentially active features in single-cell and spatial genomics data"
DOI: 10.1038/s41598-023-38965-2, Published: 2023-07-22, Citations: 6
Preprints
"A universal differential expression prediction tool for single-cell and spatial genomics data"
DOI: 10.1101/2022.11.13.516355, Citations: 2
"singleCellHaystack: Finding surprising genes in 2-dimensional representations of single cell transcriptome data"
DOI: 10.1101/557967, Citations: 0

Platform: R
Code: https://github.com/alexisvdb/singleCellHaystack
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last commitUnknown
License: MIT
Categories: Differential Expression, Marker Genes, Visualisation
Added: 2019-02-25, Updated: 2023-07-28

singleCellHaystack-py

A Python implementation of singleCellHaystack

Publications: 1, Preprints: 0, Total citations: 6

Publications
"A universal tool for predicting differentially active features in single-cell and spatial genomics data"
DOI: 10.1038/s41598-023-38965-2, Published: 2023-07-22, Citations: 6

Platform: Python
Code: https://github.com/ddiez/singleCellHaystack-py
starsN/A
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last commitUnknown
License: MIT
Categories: Marker Genes, Visualisation
Added: 2023-07-28, Updated: 2023-07-28

singleCellTK

Interactive Analysis of Single Cell RNA-Seq Data

Publications: 2, Preprints: 3, Total citations: 71

Publications
"Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data"
DOI: 10.1038/s41467-022-29212-9, Published: 2022-03-30, Citations: 61
"Interactive Analysis of Single-Cell Data Using Flexible Workflows With SCTK2.0"
DOI: 10.2139/ssrn.4174578, Published: 2022, Citations: 0
Preprints
"Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data"
DOI: 10.1101/2020.11.16.385328, Citations: 2
"Interactive analysis of single-cell data using flexible workflows with SCTK2.0"
DOI: 10.1101/2022.07.13.499900, Citations: 1
"Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)"
DOI: 10.1101/329755, Citations: 7

Platform: R
Code: https://github.com/compbiomed/singleCellTK
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last commitUnknown
License: MIT
Categories: Alignment, Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Sets, Interactive, Marker Genes, Normalisation, Quality Control, Quantification, Visualisation
Added: 2018-05-13, Updated: 2022-08-12

smartid

An R package for automatically identify group specific signature and score cells based on given gene sets on scRNA data
Platform: R
Code: https://github.com/DavisLaboratory/smartid
starsN/A
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last commitUnknown
License: MIT
Categories: Gene Sets, Marker Genes, Visualisation
Added: 2024-05-06, Updated: 2024-05-06

T

TITAN

TITAN (Topic Inference of Transcriptionally Associated Networks), an unsupervised Bayesian topic modelling based approach utilizing Latent Dirichlet Allocation (LDA)

Publications: 1, Preprints: 0, Total citations: 23

Publications
"Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer"
DOI: 10.1093/nar/gkac908, Published: 2022-11-01, Citations: 23

Platform: R
Code: https://github.com/ohsu-cedar-comp-hub/TITAN
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last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Gene Sets, Imputation, Marker Genes, Normalisation, Visualisation
Added: 2023-03-10, Updated: 2023-03-10

TSCAN

Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.

Publications: 1, Preprints: 0, Total citations: 600

Publications
"TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis"
DOI: 10.1093/nar/gkw430, Published: 2016-05-13, Citations: 600

Platform: R
Code: https://github.com/zji90/TSCAN
starsN/A
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last commitUnknown
License: GPL-2.0-or-later
Categories: Clustering, Interactive, Marker Genes, Ordering, Visualisation
Added: 2016-09-09, Updated: 2016-09-09

U

URD

URD is an R package designed for reconstructing transcriptional trajectories underlying specification or differentiation processes in the form of a branching tree, using single cell RNA-sequencing data.

Publications: 1, Preprints: 0, Total citations: 816

Publications
"Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis"
DOI: 10.1126/science.aar3131, Published: 2018-04-26, Citations: 816

Platform: R
Code: https://github.com/farrellja/URD
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Marker Genes, Normalisation, Ordering, Variable Genes, Visualisation
Added: 2018-08-24, Updated: 2018-08-24

W

WaveCrest

WaveCrest is a statistical approach to reconstruct gene expression trajectory in single cell RNA-seq experiments with ordered conditions.

Publications: 1, Preprints: 0, Total citations: 431

Publications
"Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm"
DOI: 10.1186/s13059-016-1033-x, Published: 2016-08-17, Citations: 431

Platform: R
Code: https://github.com/lengning/WaveCrest
starsN/A
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last commitUnknown
License: Apache-2.0
Categories: Expression Patterns, Interactive, Marker Genes, Ordering
Added: 2016-09-12, Updated: 2016-09-12

Wx

Wx: a neural network-based feature selection algorithm for next-generation sequencing data

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

Publications
"Wx: a neural network-based feature selection algorithm for transcriptomic data"
DOI: 10.1038/s41598-019-47016-8, Published: 2019-07-19, Citations: 10
Preprints
"Wx: a neural network-based feature selection algorithm for next-generation sequencing data"
DOI: 10.1101/221911, Citations: 1

Platform: Python
Code: https://github.com/deargen/DearWXpub
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Marker Genes
Added: 2019-07-24, Updated: 2019-07-24

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ZINBMM

A Zero-Inflated Negative Binomical mixture model for simultaneous scRNA-seq data clustering and biological relevant gene selection

Publications: 1, Preprints: 0, Total citations: 6

Publications
"ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data"
DOI: 10.1186/s13059-023-03046-0, Published: 2023-09-11, Citations: 6

Platform: R
Code: https://github.com/mengyunwu2020/ZINBMM
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0
Categories: Clustering, Marker Genes
Added: 2023-09-15, Updated: 2023-09-15

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fusca

Framework for Unified Single-Cell Analysis (FUSCA) is a software package for single-cell data analysis developed in R

Publications: 1, Preprints: 0, Total citations: 23

Publications
"CellComm infers cellular crosstalk that drives haematopoietic stem and progenitor cell development"
DOI: 10.1038/s41556-022-00884-1, Published: 2022-04-12, Citations: 23

Platform: R/Java
Code: https://github.com/edroaldo/fusca
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Cell Cycle, Clustering, Dimensionality Reduction, Gene Networks, Gene Sets, Marker Genes, Normalisation, Ordering, Variable Genes, Visualisation
Added: 2022-05-03, Updated: 2022-05-03

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jackstraw

The jackstraw package provides a statistical test to identify variables (i.e., transcripts, genes) or observations (i.e., single cells) that are significantly associated with latent variables.

Publications: 2, Preprints: 1, Total citations: 224

Publications
"Statistical significance of cluster membership for unsupervised evaluation of single cell identities"
DOI: 10.1093/bioinformatics/btaa087, Published: 2020-03-06, Citations: 23
"Statistical significance of variables driving systematic variation in high-dimensional data"
DOI: 10.1093/bioinformatics/btu674, Published: 2014-10-21, Citations: 201
Preprints
"Statistical significance of cluster membership for determination of cell identities in single cell genomics"
DOI: 10.1101/248633, Citations: 0

Platform: R
Code: https://github.com/ncchung/jackstraw
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Marker Genes, Variable Genes
Added: 2018-06-27, Updated: 2024-02-09

K

kana

kana is a web application for single-cell RNA-seq data analysis that works directly in the browser

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

Publications
"Powering single-cell analyses in the browser with WebAssembly"
DOI: 10.21105/joss.05603, Published: 2023-09-05, Citations: 6
Preprints
"Single-cell data analysis in the browser"
DOI: 10.1101/2022.03.02.482701, Citations: 4

Platform: JavaScript
Code: https://github.com/jkanche/kana
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Classification, Clustering, Dimensionality Reduction, Interactive, Marker Genes, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2022-03-11, Updated: 2024-01-05

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