scRNA-tools

A

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: 20

Publications
"Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM"
DOI: 10.1038/s43588-022-00263-8, Published: 2022-06, Citations: 19
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

AnnSql

The AnnSQL package enables SQL-based queries on AnnData objects

Publications: 0, Preprints: 1, Total 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: 2025-03-08

anticor_features

Anti-correlated genes as a method of feature selection

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

Publications
"Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq"
DOI: 10.1038/s41467-023-43406-9, Published: 2024-01-24, Citations: 6
Preprints
"Anti-correlated Feature Selection Prevents False Discovery of Subpopulations in scRNAseq"
DOI: 10.1101/2022.12.05.519161, Citations: 2

Platform: Python
Code: https://bitbucket.org/scottyler892/anticor_features
License: AGPL-3.0
Categories: Variable Genes
Added: 2022-12-09, Updated: 2024-05-06

Augur

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

Publications: 2, Preprints: 1, Total citations: 184

Publications
"Cell type prioritization in single-cell data"
DOI: 10.1038/s41587-020-0605-1, Published: 2020-07-20, Citations: 151
"Prioritization of cell types responsive to biological perturbations in single-cell data with Augur"
DOI: 10.1038/s41596-021-00561-x, Published: 2021-06-25, Citations: 31
Preprints
"Cell type prioritization in single-cell data"
DOI: 10.1101/2019.12.20.884916, Citations: 2

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

AutoImpute

AutoImpute: Autoencoder based imputation of single cell RNA-seq data

Publications: 1, Preprints: 0, Total citations: 142

Publications
"AutoImpute: Autoencoder based imputation of single-cell RNA-seq data"
DOI: 10.1038/s41598-018-34688-x, Published: 2018-11-05, Citations: 142

Platform: Python/R
Code: https://github.com/divyanshu-talwar/AutoImpute
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License: MIT
Categories: Imputation, Normalisation, Variable Genes
Added: 2018-11-09, Updated: 2018-11-09

B

BASiCS

Bayesian Analysis of single-cell RNA-seq data. Estimates cell-specific normalization constants. Technical variability is quantified based on spike-in genes. The total variability of the expression counts is decomposed into technical and biological components.

Publications: 4, Preprints: 2, Total citations: 446

Publications
"Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data"
DOI: 10.1016/j.cels.2018.06.011, Published: 2018-09, Citations: 80
"Beyond comparisons of means: understanding changes in gene expression at the single-cell level"
DOI: 10.1186/s13059-016-0930-3, Published: 2016-04-15, Citations: 92
"BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data"
DOI: 10.12688/f1000research.74416.1, Published: 2022-01-18, Citations: 0
"BASiCS: Bayesian Analysis of Single-Cell Sequencing Data"
DOI: 10.1371/journal.pcbi.1004333, Published: 2015-06-24, Citations: 271
Preprints
"Beyond comparisons of means: understanding changes in gene expression at the single-cell level"
DOI: 10.1101/035949, Citations: 2
"Robust expression variability testing reveals heterogeneous T cell responses"
DOI: 10.1101/237214, Citations: 1

Platform: R
Code: https://github.com/catavallejos/BASiCS
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License: GPL-2.0-or-later
Categories: Differential Expression, Normalisation, Simulation, Variable Genes
Added: 2016-09-08, Updated: 2022-02-04

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: 270

Publications
"bigSCale: an analytical framework for big-scale single-cell data"
DOI: 10.1101/gr.230771.117, Published: 2018-05-03, Citations: 75
"Single-cell transcriptomics unveils gene regulatory network plasticity"
DOI: 10.1186/s13059-019-1713-4, Published: 2019-06-04, Citations: 188
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

BigSur

Basic Informatics and Gene Statistics from Unnormalized Reads (BigSur) is a principled pipeline allowing for feature selection, correlation and clustering in scRNAseq

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

Publications
"Leveraging gene correlations in single cell transcriptomic data"
DOI: 10.1186/s12859-024-05926-z, Published: 2024-09-18, Citations: 3
Preprints
"Leveraging gene correlations in single cell transcriptomic data"
DOI: 10.1101/2023.03.14.532643, Citations: 2

Platform: Python/R/Mathematica
Code: https://github.com/landerlabcode/BigSur
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License: MIT
Categories: Normalisation, Quality Control, Variable Genes
Added: 2023-11-10, Updated: 2025-04-13

BITFAM

Bayesian Inference Transcription Factor Activity model (BITFAM) is based on a fundamental biological principle that the differences in scRNA-seq profiles of individual cells reflect distinct underlying transcription factor activity states

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

Publications
"A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes"
DOI: 10.1101/gr.265595.120, Published: 2021-06-30, Citations: 15

Platform: R
Code: https://github.com/jaleesr/BITFAM
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Categories: Gene Networks, Normalisation, Variable Genes
Added: 2021-07-09, Updated: 2021-07-09

C

CAPITAL

CAPITAL, a method for comparing pseudotime trajectories with tree alignment whereby trajectories including branching can be compared without any knowledge of paths to be compared.

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

Publications
"Alignment of single-cell trajectory trees with CAPITAL"
DOI: 10.1038/s41467-022-33681-3, Published: 2022-10-14, Citations: 17
Preprints
"Alignment of time-course single-cell RNA-seq data with CAPITAL"
DOI: 10.1101/859751, Citations: 0

Platform: Python
Code: https://github.com/ykat0/capital
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License: BSD-3-Clause
Categories: Dimensionality Reduction, Expression Patterns, Ordering, Quality Control, Variable Genes, Visualisation
Added: 2019-12-05, Updated: 2022-11-25

CASCC

A co-expression assisted single-cell RNA-seq data clustering method

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

Publications
"CASCC: a co-expression assisted single-cell RNA-seq data clustering method"
DOI: 10.1093/bioinformatics/btae283, Published: 2024-04-25, Citations: 0

Platform: R
Code: https://github.com/LingyiC/CASCC
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License: MIT
Categories: Clustering, Gene Filtering, Variable Genes, Visualisation
Added: 2024-05-06, Updated: 2024-05-06

cellsnake

A command line tool for easy and scalable single cell RNA sequencing analysis

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

Publications
"Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis"
DOI: 10.1093/gigascience/giad091, Published: 2022-12-28, Citations: 9
Preprints
"cellsnake: a user-friendly tool for single cell RNA sequencing analysis"
DOI: 10.1101/2023.05.03.539204, Citations: 1

Platform: Python
Code: https://github.com/sinanugur/cellsnake
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License: MIT
Categories: Classification, Clustering, Dimensionality Reduction, Integration, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2023-05-26, Updated: 2024-01-05

clustifyr

Classify cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists).

Publications: 2, Preprints: 1, Total citations: 106

Publications
"clustifyr: an R package for automated single-cell RNA sequencing cluster classification"
DOI: 10.12688/f1000research.22969.1, Published: 2020-04-01, Citations: 7
"clustifyr: an R package for automated single-cell RNA sequencing cluster classification"
DOI: 10.12688/f1000research.22969.2, Published: 2020-07-16, Citations: 96
Preprints
"clustifyr: An R package for automated single-cell RNA sequencing cluster classification"
DOI: 10.1101/855064, Citations: 3

Platform: R
Code: https://github.com/rnabioco/clustifyr
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License: MIT
Categories: Classification, Variable Genes, Visualisation
Added: 2019-12-04, Updated: 2021-03-12

CoSpar

Dynamic inference by integrating state and lineage information

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

Publications
"CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information"
DOI: 10.1038/s41587-022-01209-1, Published: 2022-02-21, Citations: 66
Preprints
"Learning dynamics by computational integration of single cell genomic and lineage information"
DOI: 10.1101/2021.05.06.443026, Citations: 2

Platform: Python
Code: https://github.com/AllonKleinLab/cospar
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License: MIT
Categories: Cell Cycle, Ordering, Variable Genes, Visualisation
Added: 2021-05-14, Updated: 2022-03-04

countClust

Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models. Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships.

Publications: 1, Preprints: 1, Total citations: 143

Publications
"Visualizing the structure of RNA-seq expression data using grade of membership models"
DOI: 10.1371/journal.pgen.1006599, Published: 2017-03-23, Citations: 141
Preprints
"Visualizing the Structure of RNA-seq Expression Data using Grade of Membership Models"
DOI: 10.1101/051631, Citations: 2

Platform: R
Code: https://github.com/kkdey/CountClust
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License: GPL-2.0-or-later
Categories: Clustering, Differential Expression, Dimensionality Reduction, Variable Genes, Visualisation
Added: 2016-09-12, Updated: 2019-11-03

D

DELVE

Dynamic selection of locally covarying features

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

Publications
"DELVE: feature selection for preserving biological trajectories in single-cell data"
DOI: 10.1038/s41467-024-46773-z, Published: 2024-03-29, Citations: 4
Preprints
"Feature selection for preserving biological trajectories in single-cell data"
DOI: 10.1101/2023.05.09.540043, Citations: 3

Platform: Python
Code: https://github.com/jranek/delve
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License: MIT
Categories: Variable Genes
Added: 2023-05-26, Updated: 2024-04-12

DESC

DESC is an unsupervised deep learning algorithm for clustering scRNA-seq data.

Publications: 1, Preprints: 1, Total citations: 254

Publications
"Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis"
DOI: 10.1038/s41467-020-15851-3, Published: 2020-05-11, Citations: 248
Preprints
"Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis"
DOI: 10.1101/530378, Citations: 6

Platform: Python
Code: https://github.com/eleozzr/desc
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Categories: Clustering, Gene Filtering, Integration, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2019-02-01, Updated: 2020-08-06

DESCEND

DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts

Publications: 1, Preprints: 1, Total citations: 81

Publications
"Gene expression distribution deconvolution in single-cell RNA sequencing"
DOI: 10.1073/pnas.1721085115, Published: 2018-06-26, Citations: 75
Preprints
"Gene Expression Distribution Deconvolution in Single Cell RNA Sequencing"
DOI: 10.1101/227033, Citations: 6

Platform: R
Code: https://github.com/jingshuw/descend
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License: GPL-2.0-or-later
Categories: Differential Expression, Modality, Variable Genes, Visualisation
Added: 2017-12-20, Updated: 2017-12-20

DIscBIO

DIscBIO: A user-friendly pipeline for biomarker discovery in single-cell transcriptomics
Platform: R
Code: https://github.com/ocbe-uio/DIscBIO
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License: MIT
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Networks, Normalisation, Ordering, Quality Control, Variable Genes, Visualisation
Added: 2020-11-22, Updated: 2020-11-22

dropClust

Efficient clustering of ultra-large scRNA-seq data

Publications: 2, Preprints: 2, Total citations: 112

Publications
"Improved dropClust R package with integrative analysis support for scRNA-seq data"
DOI: 10.1093/bioinformatics/btz823, Published: 2019-11-06, Citations: 8
"dropClust: efficient clustering of ultra-large scRNA-seq data"
DOI: 10.1093/nar/gky007, Published: 2018-01-18, Citations: 101
Preprints
"dropClust: Efficient clustering of ultra-large scRNA-seq data"
DOI: 10.1101/170308, Citations: 3
"dropClust2: An R package for resource efficient analysis of large scale single cell RNA-Seq data"
DOI: 10.1101/596924, Citations: 0

Platform: R/C++
Code: https://github.com/debsin/dropClust
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License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Normalisation, Variable Genes, Visualisation
Added: 2017-08-20, Updated: 2019-11-12

DrSeq2

Quality control and analysis pipeline for parallel single cell transcriptome and epigenome data

Publications: 1, Preprints: 1, Total citations: 24

Publications
"Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data"
DOI: 10.1371/journal.pone.0180583, Published: 2017-07-03, Citations: 21
Preprints
"Dr.seq2: a quality control and analysis pipeline for parallel single cell transcriptome and epigenome data"
DOI: 10.1101/143271, Citations: 3

Platform: Python
Code: https://github.com/ChengchenZhao/DrSeq2
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License: GPL-3.0
Categories: Alignment, Clustering, Dimensionality Reduction, Integration, Normalisation, Quality Control, Quantification, UMIs, Variable Genes
Added: 2017-06-26, Updated: 2017-09-25

DTWScore

Transcriptional heterogeneity analysis for time-series single-cell RNA-seq data

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

Publications
"DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data"
DOI: 10.1186/s12859-017-1647-3, Published: 2017-05-23, Citations: 12

Platform: R
Code: https://github.com/xiaoxiaoxier/DTWscore
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Categories: Differential Expression, Ordering, Variable Genes
Added: 2017-05-30, Updated: 2017-05-30

DUBStepR

DUBStepR (Determining the Underlying Basis using Step-wise Regression) is a feature selection algorithm for cell type identification in single-cell RNA-sequencing data

Publications: 1, Preprints: 1, Total citations: 54

Publications
"DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data"
DOI: 10.1038/s41467-021-26085-2, Published: 2021-10-06, Citations: 51
Preprints
"DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data"
DOI: 10.1101/2020.10.07.330563, Citations: 3

Platform: R
Code: https://github.com/prabhakarlab/DUBStepR
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License: MIT
Categories: Variable Genes
Added: 2020-10-15, Updated: 2021-10-18

E

EDGE

Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data

Publications: 1, Preprints: 0, Total citations: 31

Publications
"Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data"
DOI: 10.1038/s41467-020-19465-7, Published: 2020-11-17, Citations: 31

Platform: R/C++
Code: https://github.com/shawnstat/EDGE
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License: GPL-3.0
Categories: Dimensionality Reduction, Variable Genes
Added: 2020-11-22, Updated: 2020-11-22

ESCORT

Escort is a framework for evaluating a single-cell RNA-seq dataset’s suitability for trajectory inference and for quantifying trajectory properties influenced by analysis decisions

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

Publications
"Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference"
DOI: 10.1093/bib/bbae216, Published: 2024-03-27, Citations: 4
Preprints
"Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference"
DOI: 10.1101/2023.12.18.572214, Citations: 0

Platform: R
Code: https://github.com/xiaorudong/Escort
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License: MIT
Categories: Clustering, Differential Expression, Dimensionality Reduction, Interactive, Ordering, Variable Genes
Added: 2024-01-05, Updated: 2025-04-13

F

FEAST

FEAST is a framework designed for ranking features and selecting an optimized feature set as an input for scRNA-seq clustering

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

Publications
"Accurate feature selection improves single-cell RNA-seq cell clustering"
DOI: 10.1093/bib/bbab034, Published: 2021-02-22, Citations: 44

Platform: R/C
Code: https://github.com/suke18/FEAST
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License: GPL-2.0
Categories: Clustering, Variable Genes
Added: 2021-02-26, Updated: 2021-05-21

FiRE

FiRE - Finder of Rare Entities

Publications: 1, Preprints: 0, Total citations: 119

Publications
"Discovery of rare cells from voluminous single cell expression data"
DOI: 10.1038/s41467-018-07234-6, Published: 2018-11-09, Citations: 119

Platform: Python/C++/R
Code: https://github.com/princethewinner/FiRE
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License: GPL-3.0
Categories: Dimensionality Reduction, Rare Cells, Variable Genes, Visualisation
Added: 2018-11-13, Updated: 2021-09-17

FIRM

Fast Integration of single-cell RNA-sequencing data across Multiple platforms

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

Publications
"FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets"
DOI: 10.1093/bib/bbac167, Published: 2022-05-14, Citations: 17
Preprints
"FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms"
DOI: 10.1101/2020.06.02.129031, Citations: 0

Platform: R/C++
Code: https://github.com/mingjingsi/FIRM
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Categories: Integration, Variable Genes
Added: 2020-06-26, Updated: 2022-06-17

FSCAM

FSCAM enables users to easily select features from single-cell RNA sequencing

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

Publications
"FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq"
DOI: 10.1007/s12539-021-00495-8, Published: 2022-01-14, Citations: 3

Platform: R
Code: https://github.com/YanWang-jn/FSCAM
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License: GPL-2.0-or-later
Categories: Variable Genes
Added: 2022-01-22, Updated: 2022-01-22

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: 19

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: 19

Platform: R/Java
Code: https://github.com/edroaldo/fusca
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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

G

GeneClust

GeneClust is a computational feature selection method for scRNA-seq cell clustering. GeneClust groups genes into clusters from which genes are evaluated and selected with the aim of maximizing relevance, minimizing redundancy and preserving complementarity.

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

Publications
"A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis"
DOI: 10.1093/bib/bbad042, Published: 2023-02-08, Citations: 9

Platform: Python
Code: https://github.com/ToryDeng/scGeneClust
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License: AGPL-3.0
Categories: Clustering, Dimensionality Reduction, Normalisation, Variable Genes
Added: 2023-02-17, Updated: 2023-02-17

GPcounts

GPcounts is Gaussian process regression package for counts data with negative binomial and zero-inflated negative binomial likelihoods

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

Publications
"Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments"
DOI: 10.1093/bioinformatics/btab486, Published: 2021-07-02, Citations: 34
Preprints
"Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments"
DOI: 10.1101/2020.07.29.227207, Citations: 4

Platform: Python
Code: https://github.com/ManchesterBioinference/GPcounts
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License: Apache-2.0
Categories: Differential Expression, Expression Patterns, Ordering, Variable Genes, Visualisation
Added: 2020-08-06, Updated: 2021-07-27

GSFA

Bayesian sparse factor analysis on single-cell CRISPR screen data

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

Publications
"A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening"
DOI: 10.1038/s41592-023-02017-4, Published: 2023-09-28, Citations: 18
Preprints
"A novel Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening"
DOI: 10.1101/2022.02.13.480282, Citations: 1

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

I

IBRAP

IBRAP is an R-based package that has integrated many scRNA-seq analytical methods into a single pipeline

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

Publications
"IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline"
DOI: 10.1093/bib/bbad061, Published: 2023-02-27, Citations: 5
Preprints
"IBRAP: Integrated Benchmarking Single-cell RNA-sequencing Analytical Pipeline"
DOI: 10.1101/2022.09.26.509481, Citations: 0

Platform: R
Code: https://github.com/connorhknight/IBRAP
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License: GPL-3.0
Categories: Cell Cycle, Classification, Clustering, Differential Expression, Gene Sets, Integration, Normalisation, Ordering, Quality Control, Variable Genes, Visualisation
Added: 2023-05-05, Updated: 2023-05-05

IEntropy

A feature selection method, intrinsic entropy (IE) model, to identify the informative genes for accurately clustering analysis

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

Publications
"Intrinsic entropy model for feature selection of scRNA-seq data"
DOI: 10.1093/jmcb/mjac008, Published: 2022-01-31, Citations: 8

Platform: R
Code: https://github.com/LinLi-0909/IEntropy
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Categories: Variable Genes
Added: 2022-02-11, Updated: 2022-02-11

infohet

Package for the quantification of the information content of single-cell RNA-sequencing data-sets, and how much of this information has been explained by clustering

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

Publications
"An information-theoretic approach to single cell sequencing analysis"
DOI: 10.1186/s12859-023-05424-8, Published: 2023-08-12, Citations: 3
Preprints
"Information-theoretic clustering for single-cell transcriptomics"
DOI: 10.1101/2020.10.01.322255, Citations: 1

Platform: R
Code: https://github.com/mjcasy/infohet
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License: MIT
Categories: Variable Genes
Added: 2020-10-15, Updated: 2024-01-05

InGene

Find relevant genes from tSNE and UMAP projections of scRNA-seq data

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

Preprints
"InGene: Finding influential genes from embeddings of nonlinear dimension reduction techniques"
DOI: 10.1101/2023.06.19.545592, Citations: 0

Platform: R
Code: https://github.com/cgiiitd/InGene
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Variable Genes, Visualisation
Added: 2023-07-07, Updated: 2023-07-07

iS-CellR

iS-CellR (Interactive platform for Single-cell RNAseq) is a web-based Shiny application designed to provide a comprehensive analysis of single-cell RNA sequencing data.

Publications: 1, Preprints: 0, Total citations: 21

Publications
"iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data"
DOI: 10.1093/bioinformatics/bty517, Published: 2018-07-04, Citations: 21

Platform: R
Code: https://github.com/immcore/iS-CellR
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Interactive, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2018-11-09, Updated: 2018-11-09

iSORT

iSORT is an computational tool to integrate scRNA-seq data and spatial transcriptomics (ST) data

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

Preprints
"iSORT: An Integrative Method for Reconstructing Spatial Organization of Cells using Transfer Learning"
DOI: 10.1101/2024.02.28.582493, Citations: 0

Platform: Python
Code: https://github.com/xiaojierzi/iSORT
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Integration, Variable Genes
Added: 2024-03-08, Updated: 2024-09-15

J

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: 205

Publications
"Statistical significance of cluster membership for unsupervised evaluation of single cell identities"
DOI: 10.1093/bioinformatics/btaa087, Published: 2020-03-06, Citations: 20
"Statistical significance of variables driving systematic variation in high-dimensional data"
DOI: 10.1093/bioinformatics/btu674, Published: 2014-10-21, Citations: 185
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
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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: 4

Publications
"Powering single-cell analyses in the browser with WebAssembly"
DOI: 10.21105/joss.05603, Published: 2023-09-05, Citations: 1
Preprints
"Single-cell data analysis in the browser"
DOI: 10.1101/2022.03.02.482701, Citations: 3

Platform: JavaScript
Code: https://github.com/jkanche/kana
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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

L

LAmbDA

LAmbDA is an algorithm that assigns unknown labels to datasets and trains on those labels across multiple datasets.

Publications: 1, Preprints: 1, Total citations: 36

Publications
"LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection"
DOI: 10.1093/bioinformatics/btz295, Published: 2019-04-30, Citations: 34
Preprints
"LAmbDA: Label Ambiguous Domain Adaption Dataset Integration Reduces Batch Effects and Improves Subtype Detection: Supplementary Materials"
DOI: 10.1101/522474, Citations: 2

Platform: Python
Code: https://github.com/tsteelejohnson91/LAmbDA
starsN/A
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last commitUnknown
Categories: Classification, Integration, Variable Genes
Added: 2019-02-01, Updated: 2019-02-01

LIGER

LIGER (Linked Inference of Genomic Experimental Relationships)

Publications: 3, Preprints: 3, Total citations: 1239

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: 1022
"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: 54
"Jointly defining cell types from multiple single-cell datasets using LIGER"
DOI: 10.1038/s41596-020-0391-8, Published: 2020-10-12, Citations: 136
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: 22

Platform: R/C++
Code: https://github.com/welch-lab/liger
starsN/A
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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: 2023-11-17

Linnorm

Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data.
Platform: R
Code: https://github.com/kenshunyip/Linnorm
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Clustering, Differential Expression, Gene Filtering, Gene Networks, Imputation, Normalisation, Simulation, Variable Genes, Visualisation
Added: 2017-02-01, Updated: 2018-03-14

M

Matilda

Matilda is a multi-task framework for learning from single-cell multimodal omics data

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

Publications
"Multi-task learning from multimodal single-cell omics with Matilda"
DOI: 10.1093/nar/gkad157, Published: 2023-03-13, Citations: 17
Preprints
"Multi-task learning from single-cell multimodal omics with Matilda"
DOI: 10.1101/2022.06.01.494441, Citations: 1

Platform: Python
Code: https://github.com/liuchunlei0430/Matilda
starsN/A
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last commitUnknown
License: Apache-2.0
Categories: Classification, Dimensionality Reduction, Simulation, Variable Genes, Visualisation
Added: 2022-06-10, Updated: 2025-01-30

MCXpress

MCA is a dimensionality reduction technique that allows the representation of both individuals (cells) and the variables (genes) within the same Euclidean space, thus allowing the simultaneous identification of subpopulations of cells and their gene signatures.
Platform: R
Code: https://github.com/ARTbio/MCXpress
starsN/A
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last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Gene Sets, Interactive, Variable Genes
Added: 2018-03-15, Updated: 2021-06-28

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: 296

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: 284
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
starsN/A
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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

MetaNeighbor

MetaNeighbor: a method to rapidly assess cell type identity using both functional and random gene sets

Publications: 2, Preprints: 1, Total citations: 295

Publications
"Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor"
DOI: 10.1038/s41467-018-03282-0, Published: 2018-02-28, Citations: 262
"Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor"
DOI: 10.1038/s41596-021-00575-5, Published: 2021-07-07, Citations: 30
Preprints
"Addressing the looming identity crisis in single cell RNA-seq"
DOI: 10.1101/150524, Citations: 3

Platform: R
Code: https://github.com/gillislab/MetaNeighbor
starsN/A
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last commitUnknown
License: MIT
Categories: Classification, Clustering, Gene Sets, Variable Genes, Visualisation
Added: 2017-09-09, Updated: 2021-07-16

mosaicMPI

mosaicMPI is a Python package enabling mosaic integration of bulk, single-cell, and spatial expression data through program-level integration

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

Publications
"mosaicMPI: a framework for modular data integration across cohorts and -omics modalities"
DOI: 10.1093/nar/gkae442, Published: 2024-05-30, Citations: 0
Preprints
"mosaicMPI: a framework for modular data integration across cohorts and -omics modalities"
DOI: 10.1101/2023.08.18.553919, Citations: 0

Platform: Python
Code: https://github.com/MorrissyLab/mosaicMPI
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last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Gene Sets, Integration, Variable Genes, Visualisation
Added: 2023-08-24, Updated: 2025-04-13

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

MUFFIN

A suite for the analysis of functional sequencing data

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

Publications
"MUFFIN: a suite of tools for the analysis of functional sequencing data"
DOI: 10.1093/nargab/lqae051, Published: 2024-04-04, Citations: 0
Preprints
"MUFFIN : A suite of tools for the analysis of functional sequencing data"
DOI: 10.1101/2023.12.11.570597, Citations: 0

Platform: Python
Code: https://github.com/pdelangen/Muffin
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Sets, Normalisation, Variable Genes, Visualisation
Added: 2024-01-05, Updated: 2025-04-13

P

PALMO

PALMO (Platform for Analyzing Longitudinal Multi-omics data) is a platform for anayzing longitudinal data from bulk as well as single cell

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

Publications
"A comprehensive platform for analyzing longitudinal multi-omics data"
DOI: 10.1038/s41467-023-37432-w, Published: 2023-03-27, Citations: 29
Preprints
"PALMO: a comprehensive platform for analyzing longitudinal multi-omics data"
DOI: 10.1101/2022.10.17.512585, Citations: 0

Platform: R
Code: https://github.com/aifimmunology/PALMO
starsN/A
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last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Integration, Variable Genes, Visualisation
Added: 2022-05-20, Updated: 2023-05-12

Pegasus

Pegasus is a tool for analyzing transcriptomes of millions of single cells

Publications: 1, Preprints: 1, Total citations: 196

Publications
"Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq"
DOI: 10.1038/s41592-020-0905-x, Published: 2020-07-27, Citations: 188
Preprints
"Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq"
DOI: 10.1101/823682, Citations: 8

Platform: Python
Code: https://github.com/klarman-cell-observatory/pegasus
starsN/A
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last commitUnknown
License: BSD-3-Clause
Categories: Clustering, Differential Expression, Dimensionality Reduction, Integration, Normalisation, Ordering, Quality Control, Variable Genes, Visualisation
Added: 2020-08-06, Updated: 2020-08-06

phiclust

A package to calculate clusterability in clusters of scRNA-seq data

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

Publications
"Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations"
DOI: 10.1186/s13059-021-02590-x, Published: 2022-01-10, Citations: 7
Preprints
"A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations"
DOI: 10.1101/2021.05.11.443685, Citations: 0

Platform: R
Code: https://github.com/semraulab/phiclust
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Variable Genes, Visualisation
Added: 2022-01-22, Updated: 2022-01-22

Piccolo

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

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

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: 0
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
forksN/A
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

pseudoga

Cell pseudotime reconstruction based on genetic algorithm

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

Publications
"PseudoGA: cell pseudotime reconstruction based on genetic algorithm"
DOI: 10.1093/nar/gkab457, Published: 2021-07-09, Citations: 7

Platform: R
Code: https://github.com/indranillab/pseudoga
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0-or-later
Categories: Ordering, Variable Genes
Added: 2021-07-16, Updated: 2021-07-16

pyNVR

Python implementation of NVR (neighborhood variance ratio) gene selection to select genes with local and monotonic variation

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

Publications
"pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction"
DOI: 10.1093/bioinformatics/bty950, Published: 2018-11-16, Citations: 11

Platform: Python
Code: https://github.com/KenLauLab/NVR
starsN/A
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last commitUnknown
License: GPL-2.0
Categories: Variable Genes
Added: 2018-12-01, Updated: 2018-12-01

R

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: 4

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: 3
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
forksN/A
last commitUnknown
Categories: Clustering, Gene Sets, Marker Genes, Ordering, Variable Genes, Visualisation
Added: 2020-03-18, Updated: 2020-03-18

RgCop

Regularized Copula based Feature Selection

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

Publications
"RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data"
DOI: 10.1371/journal.pcbi.1009464, Published: 2021-10-19, Citations: 11
Preprints
"RgCop-A regularized copula based method for gene selection in single cell rna-seq data"
DOI: 10.1101/2020.12.23.424205, Citations: 1

Platform: R
Code: https://github.com/Snehalikalall/RgCop
starsN/A
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last commitUnknown
Categories: Variable Genes
Added: 2021-11-29, Updated: 2021-11-29

S

SCALA

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

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

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: 3
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
starsN/A
forksN/A
last commitUnknown
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

SCANet

SCANet, a python package that incorporates the inference of gene co-expression networks from single-cell gene expression data

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

Publications
"Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet"
DOI: 10.1093/bioinformatics/btad644, Published: 2023-10-20, Citations: 5

Platform: Python/R
Code: https://github.com/oubounyt/SCANet
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Gene Networks, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2023-11-03, Updated: 2023-11-03

scANNA

scANNA, a novel interpretable DL model for scR-NAseq studies that leverages neural attention to learn gene associations

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

Preprints
"Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention"
DOI: 10.1101/2023.05.29.542760, Citations: 1

Platform: Python
Code: https://github.com/SindiLab/scANNA
starsN/A
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last commitUnknown
License: MIT
Categories: Classification, Variable Genes
Added: 2023-07-01, Updated: 2023-07-01

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: 6

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: 6

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

Scanpy

Scanpy is a scalable toolkit for analyzing single-cell gene expression data.

Publications: 2, Preprints: 2, Total citations: 6848

Publications
"SCANPY: large-scale single-cell gene expression data analysis"
DOI: 10.1186/s13059-017-1382-0, Published: 2018-02-06, Citations: 5638
"PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells"
DOI: 10.1186/s13059-019-1663-x, Published: 2019-03-19, Citations: 1166
Preprints
"Scanpy for analysis of large-scale single-cell gene expression data"
DOI: 10.1101/174029, Citations: 18
"Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells"
DOI: 10.1101/208819, Citations: 26

Platform: Python
Code: https://github.com/theislab/scanpy
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Normalisation, Ordering, Quality Control, Simulation, Variable Genes, Visualisation
Added: 2017-05-21, Updated: 2019-03-25

scarf

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

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

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: 22
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
forksN/A
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

scCobra

Contrastive cell embedding learning with domain adaptation for single-cell data integration

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

Publications
"scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization"
DOI: 10.1038/s42003-025-07692-x, Published: 2025-02-13, Citations: 2
Preprints
"scCobra: Contrastive cell embedding learning with domain-adaptation for single-cell data integration"
DOI: 10.1101/2022.10.23.513389, Citations: 0

Platform: Python
Code: https://github.com/GlancerZ/scCobra
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Integration, Normalisation, Variable Genes
Added: 2022-10-28, Updated: 2025-04-13

scDHMap

scDHMap can be used for various dimensionality reduction tasks including revealing trajectory branches, batch correction, and denoising highly dropout counts

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

Publications
"Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning"
DOI: 10.1101/gr.277068.122, Published: 2023-02-27, Citations: 10

Platform: Python
Code: https://github.com/ttgump/scDHMap
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Dimensionality Reduction, Variable Genes
Added: 2023-05-05, Updated: 2023-05-05

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
forksN/A
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

sceb

Empirical Bayes estimators for single-cell RNA-Seq analysis

Publications: 0, Preprints: 1, Total citations: 17

Preprints
"One read per cell per gene is optimal for single-cell RNA-Seq"
DOI: 10.1101/389296, Citations: 17

Platform: Python
Code: https://github.com/martinjzhang/single_cell_eb
starsN/A
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last commitUnknown
Categories: Variable Genes
Added: 2018-08-24, Updated: 2018-08-24

scEC

Single-cell Entropic Clustering (scEC) provides an entropy based approach to normalisation, feature selection, differential expression analysis and unsupervised clustering of single-cell RNA-sequencing data

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

Preprints
"Information-theoretic clustering for single-cell transcriptomics"
DOI: 10.1101/2020.10.01.322255, Citations: 1

Platform: R/Python
Code: https://github.com/mjcasy/scEC
starsN/A
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last commitUnknown
License: MIT
Categories: Clustering, Differential Expression, Variable Genes
Added: 2022-03-11, Updated: 2022-03-11

SCEnt

SCEnt is a package for single cell entropy analysis
Platform: R
Code: https://github.com/hwarden162/SCEnt
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Gene Filtering, Variable Genes
Added: 2021-10-02, Updated: 2021-10-02

scFseCluster

scFseCluster: a Feature selection enhanced clustering for single cell RNA-seq data

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

Publications
"scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data"
DOI: 10.26508/lsa.202302103, Published: 2023-10-03, Citations: 5

Platform: Python
Code: https://github.com/wzqwtt/scFseClusterV1.0
starsN/A
forksN/A
last commitUnknown
Categories: Clustering, Variable Genes
Added: 2023-10-27, Updated: 2023-10-27

scGNN

scGNN (single cell graph neural networks) provides a hypothesis-free deep learning framework for scRNA-Seq analyses

Publications: 1, Preprints: 1, Total citations: 223

Publications
"scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses"
DOI: 10.1038/s41467-021-22197-x, Published: 2021-03-25, Citations: 218
Preprints
"scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses"
DOI: 10.1101/2020.08.02.233569, Citations: 5

Platform: Python
Code: https://github.com/scgnn/scGNN
starsN/A
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last commitUnknown
License: MIT
Categories: Clustering, Imputation, Variable Genes
Added: 2020-08-14, Updated: 2020-08-14

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
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Marker Genes, Variable Genes, Visualisation
Added: 2018-07-18, Updated: 2019-11-03

SciBet

A single-cell classifier that accurately predicts cell identity for newly sequenced cells or cell clusters

Publications: 1, Preprints: 1, Total citations: 121

Publications
"SciBet as a portable and fast single cell type identifier"
DOI: 10.1038/s41467-020-15523-2, Published: 2020-04-14, Citations: 119
Preprints
"SciBet: a portable and fast single cell type identifier"
DOI: 10.1101/645358, Citations: 2

Platform: R/C++
Code: https://github.com/PaulingLiu/scibet
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Classification, Variable Genes
Added: 2021-05-21, Updated: 2021-05-21

scITD

Single-Cell Interpretable Tensor Decomposition (scITD) is computational method capable of extracting multicellular gene expression programs that vary across donors or samples

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

Publications
"Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition"
DOI: 10.1038/s41587-024-02411-z, Published: 2024-09-23, Citations: 4
Preprints
"Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals"
DOI: 10.1101/2022.02.16.480703, Citations: 14

Platform: R/C++
Code: https://github.com/kharchenkolab/scITD
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Gene Networks, Integration, Normalisation, Variable Genes, Visualisation
Added: 2021-11-05, Updated: 2025-04-13

scLVM

scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation. scLVM was primarily designed to account for cell-cycle induced variations in single-cell RNA-seq data where cell cycle is the primary soure of variability.

Publications: 1, Preprints: 0, Total citations: 1073

Publications
"Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells"
DOI: 10.1038/nbt.3102, Published: 2015-01-19, Citations: 1073

Platform: R/Python
Code: https://github.com/PMBio/scLVM
starsN/A
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last commitUnknown
License: Apache-2.0
Categories: Cell Cycle, Normalisation, Variable Genes, Visualisation
Added: 2016-09-08, Updated: 2016-12-08

SCMarker

SCMarker is a R package that performs ab initio marker selection from single cell RNA sequencing data to enhance downstream cell-type clustering, trajectory inference and cell-type specific biological analysis.

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

Publications
"SCMarker: Ab initio marker selection for single cell transcriptome profiling"
DOI: 10.1371/journal.pcbi.1007445, Published: 2019-10-28, Citations: 33
Preprints
"SCMarker: ab initio marker selection for single cell transcriptome profiling"
DOI: 10.1101/356634, Citations: 0

Platform: R
Code: https://github.com/KChen-lab/SCMarker
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0-or-later
Categories: Variable Genes
Added: 2018-07-18, Updated: 2018-07-18

scMEGA

Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference

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

Publications
"scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference"
DOI: 10.1093/bioadv/vbad003, Published: 2023-01-01, Citations: 15
Preprints
"scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference"
DOI: 10.1101/2022.08.10.503335, Citations: 0

Platform: R/C++
Code: https://github.com/CostaLab/scMEGA
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Gene Networks, Ordering, Variable Genes, Visualisation
Added: 2022-08-26, Updated: 2023-05-12

SCMER

SCMER is a feature selection methods designed for single-cell data analysis

Publications: 1, Preprints: 1, Total citations: 37

Publications
"Single-cell manifold-preserving feature selection for detecting rare cell populations"
DOI: 10.1038/s43588-021-00070-7, Published: 2021-05-20, Citations: 36
Preprints
"Single-Cell Manifold Preserving Feature Selection (SCMER)"
DOI: 10.1101/2020.12.01.407262, Citations: 1

Platform: Python
Code: https://github.com/KChen-lab/SCMER
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Variable Genes
Added: 2020-12-13, Updated: 2021-05-28

scMerge

scMerge removes unwanted variation by combining stably expressed genes and utilizing pseudo-replicates across datasets

Publications: 3, Preprints: 3, Total citations: 247

Publications
"Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2"
DOI: 10.1038/s41467-023-39923-2, Published: 2023-07-17, Citations: 11
"scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets"
DOI: 10.1073/pnas.1820006116, Published: 2019-04-26, Citations: 155
"Evaluating stably expressed genes in single cells"
DOI: 10.1093/gigascience/giz106, Published: 2019-09-16, Citations: 62
Preprints
"Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2"
DOI: 10.1101/2022.12.08.519588, Citations: 2
"Evaluating stably expressed genes in single cells"
DOI: 10.1101/229815, Citations: 8
"scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication"
DOI: 10.1101/393280, Citations: 9

Platform: R
Code: https://github.com/SydneyBioX/scMerge
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Integration, Normalisation, Variable Genes
Added: 2018-08-17, Updated: 2024-01-05

scPanoView

PanoView is an iterative PCA-based method that integrates with a novel density-based clustering, ordering local maximum by convex hull (OLMC) algorithm, to identify cell subpopulations for single-cell RNA-sequencing.

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

Publications
"PanoView: An iterative clustering method for single-cell RNA sequencing data"
DOI: 10.1371/journal.pcbi.1007040, Published: 2019-08-30, Citations: 15
Preprints
"PanoView: An iterative clustering for single-cell RNA sequencing data"
DOI: 10.1101/616862, Citations: 1

Platform: Python
Code: https://github.com/mhu10/scPanoView
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Variable Genes, Visualisation
Added: 2019-09-09, Updated: 2019-09-09

scPNMF

scPNMF is a method to facilitate gene selection for targeted gene profiling by learning a sparse gene encoding of single cells

Publications: 1, Preprints: 1, Total citations: 21

Publications
"scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling"
DOI: 10.1093/bioinformatics/btab273, Published: 2021-07-01, Citations: 19
Preprints
"scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling"
DOI: 10.1101/2021.02.09.430550, Citations: 2

Platform: R
Code: https://github.com/JSB-UCLA/scPNMF
starsN/A
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last commitUnknown
License: MIT
Categories: Variable Genes
Added: 2021-02-12, Updated: 2021-07-27

scPopCorn

A python tool to do comparative analysis of mulitple single cell RNA-seq datasets.

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

Publications
"Subpopulation Detection and Their Comparative Analysis across Single-Cell Experiments with scPopCorn"
DOI: 10.1016/j.cels.2019.05.007, Published: 2019-06, Citations: 13
Preprints
"Accurate sub-population detection and mapping across single cell experiments with PopCorn"
DOI: 10.1101/485979, Citations: 0

Platform: Python
Code: https://github.com/ncbi/scPopCorn
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Clustering, Integration, Normalisation, Variable Genes, Visualisation
Added: 2019-06-28, Updated: 2020-03-03

scPrisma

scPrisma is a spectral analysis method, for pseudotime reconstruction, informative genes inference, filtering, and enhancement of underlying topological signals

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

Publications
"scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching"
DOI: 10.1038/s41587-023-01663-5, Published: 2023-02-27, Citations: 7
Preprints
"scPrisma: inference, filtering and enhancement of periodic signals in single-cell data using spectral template matching"
DOI: 10.1101/2022.06.07.493867, Citations: 1

Platform: Python
Code: https://github.com/nitzanlab/scPrisma
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Normalisation, Ordering, Variable Genes, Visualisation
Added: 2023-03-03, Updated: 2023-03-03

scran

This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, pool-based norms to estimate size factors, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.

Publications: 2, Preprints: 1, Total citations: 2922

Publications
"Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors"
DOI: 10.1038/nbt.4091, Published: 2018-04-02, Citations: 1951
"Pooling across cells to normalize single-cell RNA sequencing data with many zero counts"
DOI: 10.1186/s13059-016-0947-7, Published: 2016-04-27, Citations: 952
Preprints
"Correcting batch effects in single-cell RNA sequencing data by matching mutual nearest neighbours"
DOI: 10.1101/165118, Citations: 19

Platform: R
Code: https://github.com/MarioniLab/scran
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Cell Cycle, Integration, Interactive, Normalisation, Variable Genes, Visualisation
Added: 2016-09-08, Updated: 2018-04-02

scSensitiveGeneDefine

scSensitiveGeneDefine is a R package that define the sensitive genes in single-cell RNA sequencing data

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

Publications
"scSensitiveGeneDefine: sensitive gene detection in single-cell RNA sequencing data by Shannon entropy"
DOI: 10.1186/s12859-021-04136-1, Published: 2021-04-22, Citations: 6

Platform: R
Code: https://github.com/Zechuan-Chen/scSensitiveGeneDefine
starsN/A
forksN/A
last commitUnknown
Categories: Variable Genes
Added: 2021-04-30, Updated: 2021-04-30

scSpace

scSpace (single-cell and spatial position associated co-embeddings) is an integrative algorithm that integrates spatial transcriptome data to reconstruct spatial associations of single cells within scRNA-seq data

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

Publications
"Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace"
DOI: 10.1038/s41467-023-38121-4, Published: 2023-04-29, Citations: 23
Preprints
"Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace"
DOI: 10.1101/2022.05.07.491043, Citations: 2

Platform: R/Python
Code: https://github.com/ZJUFanLab/scSpace
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Integration, Normalisation, Variable Genes, Visualisation
Added: 2022-05-20, Updated: 2023-05-12

scSPARKL

scSPARKL is a simple framework for conducting variety of analysis on scRNA-seq data

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

Preprints
"scSPARKL: Apache Spark based parallel analytical framework for the downstream analysis of scRNA-seq data"
DOI: 10.1101/2023.04.07.536003, Citations: 0

Platform: Python
Code: https://github.com/asif7adil/scSparkl
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Normalisation, Quality Control, Variable Genes
Added: 2023-05-05, Updated: 2023-05-05

scUnifrac

scUnifrac is a R package to quantify cell population diversity between two single-cell transcriptome profiles, which calculates the distance, estimates the statistical significance of the distance, identifies the subpopulations that are significant different between two profiles, finds genes that mark subpopulations, and predicts cell types of subpopulations.

Publications: 1, Preprints: 1, Total citations: 43

Publications
"Quantitative assessment of cell population diversity in single-cell landscapes"
DOI: 10.1371/journal.pbio.2006687, Published: 2018-10-22, Citations: 42
Preprints
"Quantitative assessment of cell population diversity in single-cell landscapes"
DOI: 10.1101/333393, Citations: 1

Platform: R
Code: https://github.com/liuqivandy/scUnifrac
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Clustering, Dimensionality Reduction, Integration, Variable Genes, Visualisation
Added: 2018-06-01, Updated: 2018-06-01

scVEGs

scVEGs is a novel algorithm for single-cell RNA-seq (scRNA-seq) data to determine significant variably expressed genes (VEGs) using a gene expression variation model (GEVM).

Publications: 1, Preprints: 0, Total citations: 43

Publications
"Detection of high variability in gene expression from single-cell RNA-seq profiling"
DOI: 10.1186/s12864-016-2897-6, Published: 2016-08-22, Citations: 43

Platform: R
Code: https://github.com/hillas/scVEGs
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Variable Genes
Added: 2016-09-09, Updated: 2016-09-09

SelfE

SelfE is a feature selection technique offering transparency for the user. It is based on L2-minimization to determine an optimal subset of feature vectors enabling sub-space structures preservation as in the observed data.

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

Publications
"SelfE: Gene Selection via Self-Expression for Single-Cell Data"
DOI: 10.1109/TCBB.2020.2997326, Published: 2020, Citations: 1

Platform: R
Code: https://github.com/Priyadarshini-Rai/SelfE
starsN/A
forksN/A
last commitUnknown
License: Artistic-2.0
Categories: Variable Genes
Added: 2020-07-03, Updated: 2020-07-03

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: 42715

Publications
"Comprehensive Integration of Single-Cell Data"
DOI: 10.1016/j.cell.2019.05.031, Published: 2019-06, Citations: 12013
"Integrated analysis of multimodal single-cell data"
DOI: 10.1016/j.cell.2021.04.048, Published: 2021-06, Citations: 9907
"Spatial reconstruction of single-cell gene expression data"
DOI: 10.1038/nbt.3192, Published: 2015-04-13, Citations: 5222
"Integrating single-cell transcriptomic data across different conditions, technologies, and species"
DOI: 10.1038/nbt.4096, Published: 2018-04-02, Citations: 10411
"Dictionary learning for integrative, multimodal and scalable single-cell analysis"
DOI: 10.1038/s41587-023-01767-y, Published: 2023-05-25, Citations: 1378
"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: 3238
Preprints
"Integrated analysis of single cell transcriptomic data across conditions, technologies, and species"
DOI: 10.1101/164889, Citations: 54
"Integrated analysis of multimodal single-cell data"
DOI: 10.1101/2020.10.12.335331, Citations: 182
"Dictionary learning for integrative, multimodal, and scalable single-cell analysis"
DOI: 10.1101/2022.02.24.481684, Citations: 81
"Comprehensive integration of single cell data"
DOI: 10.1101/460147, Citations: 130
"Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression"
DOI: 10.1101/576827, Citations: 99

Platform: R
Code: https://github.com/satijalab/seurat
starsN/A
forksN/A
last commitUnknown
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
forksN/A
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
forksN/A
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

SIEVE

SIEVE is a strategy used for select highly Variable genes from single-cell RNA Seq data, based on random sampling for all single cells in a scRNA-seq dataset

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

Publications
"SIEVE: identifying robust single cell variable genes for single-cell RNA sequencing data"
DOI: 10.1097/BS9.0000000000000072, Published: 2021-04, Citations: 3

Platform: R
Code: https://github.com/YinanZhang522/SIEVE
starsN/A
forksN/A
last commitUnknown
Categories: Variable Genes
Added: 2021-05-07, Updated: 2021-05-07

SiFINeT

A cluster-independent feature gene sets detection method based on topology structure of gene coexpression network.

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

Publications
"SifiNet: a robust and accurate method to identify feature gene sets and annotate cells"
DOI: 10.1093/nar/gkae307, Published: 2024-04-22, Citations: 3
Preprints
"SifiNet: A robust and accurate method to identify feature gene sets and annotate cells"
DOI: 10.1101/2023.05.24.541352, Citations: 2

Platform: R/C++
Code: https://github.com/jichunxie/SifiNet
starsN/A
forksN/A
last commitUnknown
License: AGPL-3.0
Categories: Variable Genes
Added: 2023-06-02, Updated: 2025-01-30

SIMBA

SIngle-cell eMBedding Along with features

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

Publications
"SIMBA: single-cell embedding along with features"
DOI: 10.1038/s41592-023-01899-8, Published: 2023-05-29, Citations: 30
Preprints
"SIMBA: SIngle-cell eMBedding Along with features"
DOI: 10.1101/2021.10.17.464750, Citations: 8

Platform: Python
Code: https://github.com/pinellolab/simba
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Dimensionality Reduction, Gene Filtering, Normalisation, Quality Control, Variable Genes, Visualisation
Added: 2021-10-22, Updated: 2024-01-05

SINC

SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

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

Publications
"SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data"
DOI: 10.1093/bioinformatics/btz801, Published: 2019-10-24, Citations: 5

Platform: Python
Categories: Classification, Variable Genes
Added: 2019-11-03, Updated: 2019-11-03

slalom

slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity.

Publications: 1, Preprints: 1, Total citations: 113

Publications
"f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq"
DOI: 10.1186/s13059-017-1334-8, Published: 2017-11-07, Citations: 107
Preprints
"Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects"
DOI: 10.1101/087775, Citations: 6

Platform: R/Python
Code: https://github.com/bioFAM/slalom
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Dimensionality Reduction, Gene Sets, Variable Genes, Visualisation
Added: 2016-11-29, Updated: 2017-11-20

SOUP

SOUP, for Semi-sOft clUstering with Pure cells, is a clustering method for single cell data.

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

Publications
"Semisoft clustering of single-cell data"
DOI: 10.1073/pnas.1817715116, Published: 2018-12-26, Citations: 77
Preprints
"Semi-soft Clustering of Single Cell Data"
DOI: 10.1101/285056, Citations: 0

Platform: R
Code: https://github.com/lingxuez/SOUPR
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0-or-later
Categories: Clustering, Ordering, Variable Genes, Visualisation
Added: 2019-01-11, Updated: 2019-01-11

sparca

sparse compressed agglomeration for dimensionality reduction and feature selection

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

Preprints
"SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction"
arXiv: 2302.10776, Citations: 0

Platform: Python
Code: https://github.com/Neurology-AI-Program/sparca
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Variable Genes
Added: 2023-03-03, Updated: 2023-03-03

sscClust

simpler single cell RNAseq data clustering (sscClust), is a package implement mutilple functionalities which are basic procedures in single cell RNAseq data analysis, including variable genes identification, dimension reduction, clustering on reduced data.

Publications: 1, Preprints: 1, Total citations: 34

Publications
"SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data"
DOI: 10.1016/j.gpb.2018.10.003, Published: 2019-04, Citations: 32
Preprints
"SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data"
DOI: 10.1101/344242, Citations: 2

Platform: R
Code: https://github.com/Japrin/sscClust
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Integration, Variable Genes, Visualisation
Added: 2018-11-13, Updated: 2019-06-20

T

TREG

The goal of TREG is to help find candidate Total RNA Expression Genes (TREGs) in single nucleus (or single cell) RNA-seq data

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

Publications
"Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue"
DOI: 10.1186/s13059-023-03066-w, Published: 2023-10-16, Citations: 4
Preprints
"Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types"
DOI: 10.1101/2022.04.28.489923, Citations: 4

Platform: R
Code: https://github.com/LieberInstitute/TREG
starsN/A
forksN/A
last commitUnknown
License: Artistic-2.0
Categories: Variable Genes
Added: 2022-05-06, Updated: 2024-01-05

Triku

A feature selection method based on nearest neighbors for single-cell data

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

Publications
"Triku: a feature selection method based on nearest neighbors for single-cell data"
DOI: 10.1093/gigascience/giac017, Published: 2022, Citations: 17
Preprints
"Triku: a feature selection method based on nearest neighbors for single-cell data"
DOI: 10.1101/2021.02.12.430764, Citations: 3

Platform: Python
Code: https://gitlab.com/alexmascension/triku
License: BSD-3-Clause
Categories: Variable Genes
Added: 2021-02-19, Updated: 2022-03-18

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: 724

Publications
"Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis"
DOI: 10.1126/science.aar3131, Published: 2018-04-26, Citations: 724

Platform: R
Code: https://github.com/farrellja/URD
starsN/A
forksN/A
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

uSORT

This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization.
Platform: R
Code: https://github.com/JinmiaoChenLab/uSORT
starsN/A
forksN/A
last commitUnknown
License: Artistic-2.0
Categories: Differential Expression, Gene Filtering, Interactive, Ordering, Quality Control, Variable Genes
Added: 2018-08-31, Updated: 2018-08-31

V

velvet

Variational Estimation of Latent Velocity from Expression with Time-resolution

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

Publications
"Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics"
DOI: 10.1016/j.cels.2024.04.004, Published: 2024-05, Citations: 6
Preprints
"Deep dynamical modelling of developmental trajectories with temporal transcriptomics"
DOI: 10.1101/2023.07.06.547989, Citations: 5

Platform: Python
Code: https://github.com/rorymaizels/velvet
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Clustering, Normalisation, Ordering, Variable Genes, Visualisation
Added: 2023-07-14, Updated: 2025-04-13

VIVS

VIVS (Variational Inference for Variable Selection) is a Python package to identify molecular dependencies in omics data

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

Publications
"VI-VS: calibrated identification of feature dependencies in single-cell multiomics"
DOI: 10.1186/s13059-024-03419-z, Published: 2024-11-15, Citations: 0
Preprints
"Calibrated Identification of Feature Dependencies in Single-cell Multiomics"
DOI: 10.1101/2023.11.03.565520, Citations: 0

Platform: Python
Code: https://github.com/YosefLab/VIVS
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Integration, Variable Genes
Added: 2023-11-10, Updated: 2025-04-13

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I

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J

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