scRNA-tools

A

ASAP

ASAP : Automated Single-cell Analysis Pipeline

Publications: 2, Preprints: 1, Total citations: 150

Publications
"ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data"
DOI: 10.1093/bioinformatics/btx337, Published: 2017-05-24, Citations: 121
"ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses"
DOI: 10.1093/nar/gkaa412, Published: 2020-05-25, Citations: 24
Preprints
"ASAP: A web-based platform for the analysis and interactive visualization of single-cell RNA-seq data"
DOI: 10.1101/096222, Citations: 5

Platform: R/Python
Code: https://github.com/DeplanckeLab/ASAP
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License: GPL-3.0
Categories: Clustering, Differential Expression, Gene Filtering, Interactive, Normalisation, Visualisation
Added: 2018-11-12, Updated: 2023-12-22

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

adobo

adobo is a Python-framework for single cell gene expression data analysis

Publications: 1, Preprints: 0, Total citations: 37

Publications
"alona: a web server for single cell RNA-seq analysis"
DOI: 10.1093/bioinformatics/btaa269, Published: 2020-04-23, Citations: 37

Platform: Python
Code: https://github.com/oscar-franzen/adobo
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License: GPL-3.0
Categories: Cell Cycle, Classification, Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Imputation, Normalisation, Quality Control, Visualisation
Added: 2020-05-05, Updated: 2020-05-05

ascend

Analysis of Single Cell Expresssion Normalisation and Differential Expression

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

Publications
"ascend: R package for analysis of single-cell RNA-seq data"
DOI: 10.1093/gigascience/giz087, Published: 2019-08-24, Citations: 33
Preprints
"ascend: R package for analysis of single cell RNA-seq data"
DOI: 10.1101/207704, Citations: 11

Platform: R
Code: https://github.com/powellgenomicslab/ascend
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License: GPL-3.0
Categories: Cell Cycle, Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Normalisation, Quality Control, Visualisation
Added: 2017-11-20, Updated: 2019-09-23

B

BESCA

The BESCA (BEDA’s single cell sequencing analysis) package contains many useful python functions to use for your single-cell analysis

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

Publications
"Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research"
DOI: 10.1093/nargab/lqab102, Published: 2021-10-04, Citations: 18
Preprints
"Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research"
DOI: 10.1101/2020.08.11.245795, Citations: 1

Platform: Python
Code: https://github.com/bedapub/besca
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License: GPL-3.0
Categories: Classification, Clustering, Differential Expression, Gene Filtering, Gene Sets, Integration, Normalisation, Quality Control, Visualisation
Added: 2020-08-20, Updated: 2022-05-20

BackSPIN

Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.

Publications: 1, Preprints: 0, Total citations: 2985

Publications
"Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq"
DOI: 10.1126/science.aaa1934, Published: 2015-02-19, Citations: 2985

Platform: Python
Code: https://github.com/linnarsson-lab/BackSPIN
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License: BSD-2-Clause
Categories: Clustering, Gene Filtering
Added: 2016-09-08, Updated: 2019-10-02

C

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

COTAN

A statistical and computational method to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis

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

Publications
"COTAN: scRNA-seq data analysis based on gene co-expression"
DOI: 10.1093/nargab/lqab072, Published: 2021-08-11, Citations: 20

Platform: R
Code: https://github.com/seriph78/COTAN
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License: GPL-3.0
Categories: Gene Filtering, Gene Networks, Visualisation
Added: 2023-01-20, Updated: 2023-01-20

Cobolt

Cobolt is a method developed for modeling multi-omic single-cell sequencing datasets

Publications: 1, Preprints: 1, Total citations: 130

Publications
"Cobolt: integrative analysis of multimodal single-cell sequencing data"
DOI: 10.1186/s13059-021-02556-z, Published: 2021-12, Citations: 124
Preprints
"Cobolt: Joint analysis of multimodal single-cell sequencing data"
DOI: 10.1101/2021.04.03.438329, Citations: 6

Platform: Python
Code: https://github.com/epurdom/cobolt
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License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Integration, Visualisation
Added: 2021-11-12, Updated: 2022-01-06

ccfindR

single-cell RNA-seq data analysis using Bayesian non-negative matrix factorization

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

Publications
"De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments"
DOI: 10.26508/lsa.201900443, Published: 2019-07-02, Citations: 10

Platform: R
Code: https://github.com/hjunwoo/ccfindR
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License: GPL-2.0-or-later
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Normalisation, Quality Control, Simulation, Visualisation
Added: 2018-05-13, Updated: 2019-07-11

D

DESC

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

Publications: 1, Preprints: 1, Total citations: 316

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: 310
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

DIEM

Debris Identification using Expectation Maximization

Publications: 1, Preprints: 1, Total citations: 88

Publications
"Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM"
DOI: 10.1038/s41598-020-67513-5, Published: 2020-07-03, Citations: 83
Preprints
"Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM"
DOI: 10.1101/786285, Citations: 5

Platform: R/C++
Code: https://github.com/marcalva/diem
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License: GPL-3.0
Categories: Clustering, Gene Filtering, Quality Control, Visualisation
Added: 2019-10-09, Updated: 2020-01-02

DeLorean

R package to model time series accounting for noise in the temporal dimension. Specifically designed for single cell transcriptome experiments.

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

Publications
"Pseudotime estimation: deconfounding single cell time series"
DOI: 10.1093/bioinformatics/btw372, Published: 2016-06-17, Citations: 117
Preprints
"Pseudotime estimation: deconfounding single cell time series"
DOI: 10.1101/019588, Citations: 4

Platform: R
Code: https://github.com/JohnReid/DeLorean
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License: MIT
Categories: Expression Patterns, Gene Filtering, Ordering, Visualisation
Added: 2016-09-09, Updated: 2016-09-09

DendroSplit

DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets.

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

Publications
"An interpretable framework for clustering single-cell RNA-Seq datasets"
DOI: 10.1186/s12859-018-2092-7, Published: 2018-03-09, Citations: 44
Preprints
"An Interpretable Framework for Clustering Single-Cell RNA-Seq Datasets"
DOI: 10.1101/191254, Citations: 0

Platform: Python
Code: https://github.com/jessemzhang/dendrosplit
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License: CC-BY-NC-4.0
Categories: Clustering, Gene Filtering, Normalisation
Added: 2018-01-19, Updated: 2018-01-19

F

FORKS

Finding Orderings Robustly using K-means and Steiner trees (FORKS), an algorithm that pseudo-temporally orders cells and thereby infers bifurcating state trajectories.

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

Preprints
"FORKS: Finding Orderings Robustly using k-means and Steiner trees"
DOI: 10.1101/132811, Citations: 1

Platform: Python
Code: https://github.com/macsharma/FORKS
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License: GPL-3.0
Categories: Clustering, Gene Filtering, Normalisation, Ordering, Visualisation
Added: 2018-03-22, Updated: 2018-03-22

FastRNA

FastRNA is a scalable framework for single-cell RNA sequencing (scRNA-seq) analysis

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

Publications
"FastRNA: An efficient solution for PCA of single-cell RNA-sequencing data based on a batch-accounting count model"
DOI: 10.1016/j.ajhg.2022.09.008, Published: 2022-10, Citations: 6

Platform: Python
Code: https://github.com/hanbin973/FastRNA
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Categories: Dimensionality Reduction, Gene Filtering
Added: 2022-10-14, Updated: 2022-10-14

G

Granatum

This is a graphical single-cell RNA-seq (scRNA-seq) analysis pipeline for genomics scientists.

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

Publications
"Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists"
DOI: 10.1186/s13073-017-0492-3, Published: 2017-12-05, Citations: 62
Preprints
"Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists"
DOI: 10.1101/110759, Citations: 3

Platform: Virtualbox
Code: https://gitlab.com/uhcclxgg/granatum
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Networks, Gene Sets, Interactive, Normalisation, Ordering, Quality Control, Visualisation
Added: 2017-03-01, Updated: 2018-04-24

L

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
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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

MAESTRO

MAESTRO (Model-based AnalysEs of Single-cell Transcriptome and RegulOme) is a comprehensive single-cell RNA-seq and ATAC-seq analysis suit built using snakemake

Publications: 1, Preprints: 0, Total citations: 174

Publications
"Integrative analyses of single-cell transcriptome and regulome using MAESTRO"
DOI: 10.1186/s13059-020-02116-x, Published: 2020-08-07, Citations: 174

Platform: Python/R/C++
Code: https://github.com/liulab-dfci/MAESTRO
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License: GPL-3.0
Categories: Alignment, Classification, Clustering, Dimensionality Reduction, Gene Filtering, Gene Sets, Integration, Quality Control, Quantification, UMIs, Visualisation
Added: 2020-08-14, Updated: 2020-08-14

MATTE

MATTE (Module Alignment of TranscripTomE) is a python package aiming to analyze transcriptome from samples with different phenotypes in a module view

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

Publications
"MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis"
DOI: 10.1093/bib/bbad207, Published: 2023-06-03, Citations: 2
Preprints
"MATTE: anti-noise module alignment for phenotype-gene-related analysis"
DOI: 10.1101/2022.05.29.493935, Citations: 1

Platform: Python
Code: https://github.com/zjupgx/MATTE
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License: GPL-3.0
Categories: Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Sets, Normalisation, Visualisation
Added: 2022-06-03, Updated: 2023-06-30

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
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License: MIT
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Gene Sets, Interactive, Variable Genes
Added: 2018-03-15, Updated: 2021-06-28

MUDAN

Multi-sample Unified Discriminant ANalysis
Platform: R
Code: https://github.com/JEFworks/MUDAN
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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

mmutil

Lightweight single-cell data analysis by Matrix Market Utility

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

Publications
"CoCoA-diff: counterfactual inference for single-cell gene expression analysis"
DOI: 10.1186/s13059-021-02438-4, Published: 2021-08-17, Citations: 18
Preprints
"Counterfactual inference for single-cell gene expression analysis"
DOI: 10.1101/2021.01.21.21249765, Citations: 0

Platform: C++
Code: https://github.com/ypark/mmutil
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License: MIT
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Integration, Quality Control, Simulation
Added: 2021-09-03, Updated: 2021-09-03

McImpute

A matlab tool for imputation of scRNA-seq data

Publications: 1, Preprints: 1, Total citations: 70

Publications
"McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data"
DOI: 10.3389/fgene.2019.00009, Published: 2019-01-29, Citations: 67
Preprints
"McImpute: Matrix completion based imputation for single cell RNA-seq data"
DOI: 10.1101/361980, Citations: 3

Platform: MATLAB
Code: https://github.com/aanchalMongia/McImpute_scRNAseq
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Categories: Gene Filtering, Imputation, Normalisation
Added: 2018-11-16, Updated: 2018-11-16

MicroCellClust

Designed to search for rare cell-clusters (typically less than 10% of the number of cells) that present a highly specific gene expression in scRNA-seq data

Publications: 1, Preprints: 0, Total citations: 14

Publications
"MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data"
DOI: 10.1093/bioinformatics/btab239, Published: 2021-04-08, Citations: 14

Platform: Scala/R
Code: https://github.com/agerniers/MicroCellClust
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License: GPL-3.0
Categories: Clustering, Gene Filtering, Rare Cells
Added: 2021-04-16, Updated: 2023-05-26

O

OGFSC

Performs optimized gene filtering for single-cell RNA-seq data

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

Publications
"Optimal Gene Filtering for Single-Cell data (OGFSC)—a gene filtering algorithm for single-cell RNA-seq data"
DOI: 10.1093/bioinformatics/bty1016, Published: 2018-12-10, Citations: 17

Platform: MATLAB
Code: https://github.com/XZouProjects/OGFSC_matlab
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Categories: Gene Filtering
Added: 2018-12-13, Updated: 2018-12-13

P

PIVOT

PIVOT facilitates fast and interactive RNA-Seq data analysis and visualization.

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

Publications
"PIVOT: platform for interactive analysis and visualization of transcriptomics data"
DOI: 10.1186/s12859-017-1994-0, Published: 2018-01-05, Citations: 38
Preprints
"PIVOT: Platform for Interactive Analysis and Visualization of Transcriptomics Data"
DOI: 10.1101/053348, Citations: 1

Platform: R
Code: https://github.com/qinzhu/PIVOT
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License: Custom
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Networks, Gene Sets, Interactive, Normalisation, Ordering, Quality Control, Visualisation
Added: 2017-07-16, Updated: 2018-03-14

pagoda2

Single Cell Analysis and Differential Expression
Platform: R/C++
Code: https://github.com/hms-dbmi/pagoda2
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License: GPL-3.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Sets, Interactive, Normalisation, Quality Control, Visualisation
Added: 2018-11-09, Updated: 2021-01-29

R

RCSL

RCSL is an R toolkit for single-cell clustering and trajectory analysis using single-cell RNA-seq data

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

Publications
"Clustering single-cell RNA-seq data by rank constrained similarity learning"
DOI: 10.1093/bioinformatics/btab276, Published: 2021-05-07, Citations: 12
Preprints
"Clustering single-cell RNA-seq data by rank constrained similarity learning"
DOI: 10.1101/2021.04.12.439254, Citations: 0

Platform: R
Code: https://github.com/QinglinMei/RCSL
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License: GPL-3.0
Categories: Clustering, Gene Filtering, Ordering, Visualisation
Added: 2021-04-16, Updated: 2021-05-21

ROGUE

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

Publications: 1, Preprints: 1, Total citations: 135

Publications
"An entropy-based metric for assessing the purity of single cell populations"
DOI: 10.1038/s41467-020-16904-3, Published: 2020-06-22, Citations: 132
Preprints
"ROGUE: an entropy-based universal metric for assessing the purity of single cell population"
DOI: 10.1101/819581, Citations: 3

Platform: R
Code: https://github.com/PaulingLiu/ROGUE
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License: BSD-3-Clause
Categories: Clustering, Gene Filtering, Quality Control, Visualisation
Added: 2019-11-03, Updated: 2020-07-03

Randomly

A Python package for denoising single-cell data using Random Matrix Theory

Publications: 1, Preprints: 2, Total citations: 43

Publications
"A Random Matrix Theory Approach to Denoise Single-Cell Data"
DOI: 10.1016/j.patter.2020.100035, Published: 2020-06, Citations: 40
Preprints
"Quasi-universality in single-cell sequencing data"
DOI: 10.1101/426239, Citations: 3
"Quasi-universality in single-cell sequencing data"
arXiv: 1810.03602, Citations: 0

Platform: Python
Code: https://github.com/RabadanLab/randomly
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License: MIT
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Imputation, Quality Control, Visualisation
Added: 2020-05-14, Updated: 2020-05-14

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

Regionvelocity

Region velocity is an extension of RNA velocity for long-read sequencing technology using exons and introns counts as observed data to extrapolate future cell state

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

Publications
"Improving the RNA velocity approach with single-cell RNA lifecycle (nascent, mature and degrading RNAs) sequencing technologies"
DOI: 10.1093/nar/gkad969, Published: 2023-11-06, Citations: 8
Preprints
"Improving the RNA velocity approach using long-read single cell sequencing"
DOI: 10.1101/2022.05.02.490352, Citations: 0

Platform: R/C++
Code: https://github.com/Dekayzc/Regionvelocity
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License: GPL-3.0
Categories: Dimensionality Reduction, Gene Filtering, Ordering, Visualisation
Added: 2022-05-06, Updated: 2023-11-17

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

SCEnt

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

SCQC

Single Cell Sequencing Data Quality Control

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

Publications
"Screen technical noise in single cell RNA sequencing data"
DOI: 10.1016/j.ygeno.2019.02.014, Published: 2020-01, Citations: 10

Platform: R
Code: https://github.com/ybai3/scqc
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Categories: Gene Filtering, Quality Control
Added: 2019-03-01, Updated: 2019-03-01

SCell

SCell is an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.

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

Publications
"SCell: integrated analysis of single-cell RNA-seq data"
DOI: 10.1093/bioinformatics/btw201, Published: 2016-04-19, Citations: 43

Platform: MATLAB
Code: https://github.com/diazlab/SCell
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last commitUnknown
License: GPL-1.0
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Interactive, Normalisation, Ordering, Quality Control, Visualisation
Added: 2016-09-08, Updated: 2016-09-08

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

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
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Marker Genes, Normalisation, Visualisation
Added: 2018-11-09, Updated: 2020-02-13

SIMBA

SIngle-cell eMBedding Along with features

Publications: 1, Preprints: 1, Total citations: 64

Publications
"SIMBA: single-cell embedding along with features"
DOI: 10.1038/s41592-023-01899-8, Published: 2023-05-29, Citations: 56
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

sake

Single-cell RNA-Seq Analysis and Klustering Evaluation. The aim of sake is to provide a user-friendly tool for easy analysis of NGS Single-Cell transcriptomic data.
Platform: R
Code: https://github.com/naikai/sake
starsN/A
forksN/A
last commitUnknown
License: GPL-2.0-or-later
Categories: Clustering, Differential Expression, Dimensionality Reduction, Gene Filtering, Gene Networks, Gene Sets, Interactive, Normalisation, Visualisation
Added: 2016-10-10, Updated: 2016-10-10

sc-REnF

An Entropy Based Feature Selection- Application on Single cell RNA Sequence Data

Publications: 1, Preprints: 2, Total citations: 22

Publications
"sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data"
DOI: 10.1093/bib/bbab517, Published: 2022-01-17, Citations: 18
Preprints
"sc-REnF:An entropy guided robust feature selection for clustering of single-cell rna-seq data"
DOI: 10.1101/2020.10.10.334573, Citations: 2
"sc-REnF: An Entropy Guided Robust Feature Selection for Single-Cell RNA-seq Data"
DOI: 10.21203/rs.3.rs-355014/v1, Citations: 2

Platform: R
Code: https://github.com/Snehalikalall/sc-REnF
starsN/A
forksN/A
last commitUnknown
Categories: Gene Filtering
Added: 2020-10-15, Updated: 2022-03-04

scDHA

The scDHA software package can perform cell segregation through unsupervised learning, dimension reduction and visualization, cell classification, and time-trajectory inference on single-cell RNA sequencing data

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

Publications
"Fast and precise single-cell data analysis using a hierarchical autoencoder"
DOI: 10.1038/s41467-021-21312-2, Published: 2021-02-15, Citations: 121
Preprints
"Fast and precise single-cell data analysis using hierarchical autoencoder"
DOI: 10.1101/799817, Citations: 3

Platform: R/C++
Code: https://github.com/duct317/scDHA
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Classification, Clustering, Dimensionality Reduction, Gene Filtering, Ordering, Visualisation
Added: 2021-07-16, Updated: 2025-12-29

scFeatureFilter

An R package to set thresholds for feature (gene, transcript, ...) filtering in single cell RNA sequencing analyses

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

Publications
"scFeatureFilter: Correlation-Based Feature Filtering for Single-Cell RNAseq"
DOI: 10.1007/978-3-319-78723-7_31, Published: 2018-03-28, Citations: 0

Platform: R
Code: https://github.com/gdevailly/scFeatureFilter
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Gene Filtering, Visualisation
Added: 2018-05-01, Updated: 2018-05-01

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
starsN/A
forksN/A
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

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
starsN/A
forksN/A
last commitUnknown
License: Apache-2.0
Categories: Dimensionality Reduction, Gene Filtering, Marker Genes, Visualisation
Added: 2022-03-25, Updated: 2023-05-12

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

scTM

scTM is a package for spatial transcriptomics for single cell that uses topic modelling, solved with stochastic variational infernce
Platform: Python
Code: https://github.com/JinmiaoChenLab/scTM
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Gene Filtering, Integration, Visualisation
Added: 2023-08-24, Updated: 2023-08-24

scVelo

scVelo is a scalable toolkit for estimating and analyzing RNA velocities in single cells using dynamical modeling.

Publications: 1, Preprints: 1, Total citations: 2649

Publications
"Generalizing RNA velocity to transient cell states through dynamical modeling"
DOI: 10.1038/s41587-020-0591-3, Published: 2020-08-03, Citations: 2512
Preprints
"Generalizing RNA velocity to transient cell states through dynamical modeling"
DOI: 10.1101/820936, Citations: 137

Platform: Python
Code: https://github.com/theislab/scvelo
starsN/A
forksN/A
last commitUnknown
License: BSD-3-Clause
Categories: Gene Filtering, Normalisation, Ordering, Visualisation
Added: 2019-11-03, Updated: 2020-08-06

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
starsN/A
forksN/A
last commitUnknown
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

Scanpy

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

Publications: 2, Preprints: 2, Total citations: 8724

Publications
"SCANPY: large-scale single-cell gene expression data analysis"
DOI: 10.1186/s13059-017-1382-0, Published: 2018-02-06, Citations: 7278
"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: 1399
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: 29

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

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
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

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
starsN/A
forksN/A
last commitUnknown
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

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
forksN/A
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
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

scone

SCONE (Single-Cell Overview of Normalized Expression), a package for single-cell RNA-seq data quality control (QC) and normalization. This data-driven framework uses summaries of expression data to assess the efficacy of normalization workflows.

Publications: 1, Preprints: 1, Total citations: 148

Publications
"Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq"
DOI: 10.1016/j.cels.2019.03.010, Published: 2019-04, Citations: 132
Preprints
"Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-seq"
DOI: 10.1101/235382, Citations: 16

Platform: R
Code: https://github.com/YosefLab/scone
starsN/A
forksN/A
last commitUnknown
License: Artistic-2.0
Categories: Gene Filtering, Imputation, Interactive, Normalisation, Quality Control, Visualisation
Added: 2016-09-08, Updated: 2021-01-08

scprep

scprep provides an all-in-one framework for loading, preprocessing, and plotting matrices in Python, with a focus on single-cell genomics
Platform: Python
Code: https://github.com/KrishnaswamyLab/scprep
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Gene Filtering, Gene Sets, Normalisation, Quality Control, Visualisation
Added: 2019-11-03, Updated: 2021-06-28

scry

A collection of methods for the analysis of small-count data, such as single-cell RNA-seq
Platform: R
Code: https://github.com/kstreet13/scry
starsN/A
forksN/A
last commitUnknown
License: Artistic-2.0
Categories: Dimensionality Reduction, Gene Filtering
Added: 2020-04-29, Updated: 2020-04-29

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
starsN/A
forksN/A
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

singlet

Single cell RNA-Seq analysis with quantitative phenotypes.
Platform: Python
Code: https://github.com/iosonofabio/singlet
starsN/A
forksN/A
last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Normalisation, Visualisation
Added: 2017-12-20, Updated: 2017-12-20

V

VeloCycle

Manifold-constrained variational inference for RNA velocity of the cell cycle

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

Publications
"Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations"
DOI: 10.1038/s41592-024-02471-8, Published: 2024-10-31, Citations: 18
Preprints
"Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations"
DOI: 10.1101/2024.01.18.576093, Citations: 7

Platform: Python
Code: https://github.com/lamanno-epfl/velocycle
starsN/A
forksN/A
last commitUnknown
License: CC-BY-NC-ND-4.0
Categories: Cell Cycle, Gene Filtering, Normalisation, Ordering, Visualisation
Added: 2024-02-02, Updated: 2025-04-13

A

C

E

eSPRESSO

espresso is a package for domain estimation of cells only from gene expression profile based on graph-structured stochastic self-organizing map (graph-SOM) and Markov chain Monte Carlo (MCMC)

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

Publications
"eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells"
DOI: 10.1186/s12859-023-05355-4, Published: 2023-06-15, Citations: 1
Preprints
"eSPRESSO: a spatial self-organizing-map clustering method for single-cell transcriptomes of various tissue structures using graph-based networks"
DOI: 10.1101/2020.12.31.424948, Citations: 1

Platform: R
Code: https://github.com/tmorikuicr/espresso
starsN/A
forksN/A
last commitUnknown
License: GPL-3.0
Categories: Clustering, Dimensionality Reduction, Gene Filtering, Visualisation
Added: 2021-01-08, Updated: 2024-01-05

I

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: 22

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: 22

Platform: R
Code: https://github.com/immcore/iS-CellR
starsN/A
forksN/A
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

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: 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

M

P

R

S

U

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

W

wf-single-cell

wf-single-cell is a research pipeline designed to identify the cell barcode and UMI sequences present in nanopore sequencing reads generated from single-cell gene expression libraries
Platform: Python/Nextflow/Groovy
Code: https://github.com/epi2me-labs/wf-single-cell
starsN/A
forksN/A
last commitUnknown
License: MPL-2.0
Categories: Alignment, Alternative Splicing, Dimensionality Reduction, Gene Filtering, Normalisation, Quality Control, Quantification, UMIs
Added: 2022-12-16, Updated: 2022-12-16

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