scRNA-tools

C

CellSIUS

CellSIUS: Cell Subtype Identification from Upregulated gene Sets

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

Publications
"CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data"
DOI: 10.1186/s13059-019-1739-7, Published: 2019-07-17, Citations: 46
Preprints
"CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data"
DOI: 10.1101/514950, Citations: 1

Platform: R
Code: https://github.com/Novartis/CellSIUS
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last commitUnknown
License: Apache-2.0
Categories: Clustering, Rare Cells, Visualisation
Added: 2021-07-16, Updated: 2021-07-16

CIARA

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

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

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

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

CONCLUS

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

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

Preprints
"Single-cell atlas of major haematopoietic tissues sheds light on blood cell formation from embryonic endothelium"
DOI: 10.1101/774547, Citations: 3

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

F

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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last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Rare Cells, Variable Genes, Visualisation
Added: 2018-11-13, Updated: 2021-09-17

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GapClust

Detecting rare cells from expression profiles by single cell RNA-seq

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

Publications
"GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles"
DOI: 10.1038/s41467-021-24489-8, Published: 2021-07-07, Citations: 23

Platform: R
Code: https://github.com/fabotao/GapClust
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License: GPL-3.0
Categories: Clustering, Rare Cells
Added: 2021-07-16, Updated: 2021-07-16

GiniClust

GiniClust is a clustering method implemented in Python and R for detecting rare cell-types from large-scale single-cell gene expression data.

Publications: 1, Preprints: 0, Total citations: 244

Publications
"GiniClust: detecting rare cell types from single-cell gene expression data with Gini index"
DOI: 10.1186/s13059-016-1010-4, Published: 2016-07-01, Citations: 244

Platform: R/Python
Code: https://github.com/lanjiangboston/GiniClust
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last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Interactive, Normalisation, Rare Cells
Added: 2016-09-09, Updated: 2016-09-09

GiniClust3

GiniClust is a clustering method specifically designed for rare cell type detection. In this new version (GiniClust3), we have substantially increased the speed and reduced memory usage in order to meet the need for large data size.

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

Publications
"GiniClust3: a fast and memory-efficient tool for rare cell type identification"
DOI: 10.1186/s12859-020-3482-1, Published: 2020-04-25, Citations: 34
Preprints
"GiniClust3: a fast and memory-efficient tool for rare cell type identification"
DOI: 10.1101/788554, Citations: 0

Platform: Python
Code: https://github.com/rdong08/GiniClust3
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last commitUnknown
License: MIT
Categories: Clustering, Dimensionality Reduction, Rare Cells, Visualisation
Added: 2019-09-30, Updated: 2019-10-09

I

ILoReg

ILoReg is a tool for high-resolution cell population identification from single-cell RNA-seq (scRNA-seq) data. High-resolution refers to the ability of ILoReg to identify subpopulations with subtle transcriptomic differences.

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

Publications
"ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data"
DOI: 10.1093/bioinformatics/btaa919, Published: 2020-12-13, Citations: 4
Preprints
"ILoReg enables high-resolution cell population identification from single-cell RNA-seq data"
DOI: 10.1101/2020.01.20.912675, Citations: 1

Platform: R
Code: https://github.com/elolab/iloreg
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Rare Cells
Added: 2020-02-01, Updated: 2020-11-15

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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: 11

Publications
"MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data"
DOI: 10.1093/bioinformatics/btab239, Published: 2021-04-08, Citations: 11

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

R

RaceID

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

Publications: 4, Preprints: 1, Total citations: 1710

Publications
"De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data"
DOI: 10.1016/j.stem.2016.05.010, Published: 2016-08, Citations: 522
"Single-cell messenger RNA sequencing reveals rare intestinal cell types"
DOI: 10.1038/nature14966, Published: 2015-08-19, Citations: 1092
"Revealing dynamics of gene expression variability in cell state space"
DOI: 10.1038/s41592-019-0632-3, Published: 2019-11-18, Citations: 79
"VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells"
DOI: 10.1186/s13059-023-02974-1, Published: 2023-06-23, Citations: 12
Preprints
"Gene expression noise dynamics unveil functional heterogeneity of ageing hematopoietic stem cells"
DOI: 10.1101/2022.08.04.502776, Citations: 5

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

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SCA

Shannon Components Analysis (formerly called scalpel) is a dimensionality reduction technique for single-cell data which leverages mathematical information theory to identify biologically informative axes of variation in single-cell transcriptomic data

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

Preprints
"Scalpel: Information-based Dimensionality Reduction for Rare Cell Type Discovery"
DOI: 10.1101/2021.01.19.427303, Citations: 1

Platform: Python
Code: https://github.com/bendemeo/shannonca
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last commitUnknown
License: MIT
Categories: Dimensionality Reduction, Rare Cells
Added: 2021-01-22, Updated: 2021-10-29

scAlign

A deep learning method for aligning and integrating scRNA-seq data collected across multiple conditions into a common, low-dimensional cell state space for downstream analysis

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

Publications
"scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data"
DOI: 10.1186/s13059-019-1766-4, Published: 2019-08-14, Citations: 77
Preprints
"scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data"
DOI: 10.1101/504944, Citations: 6

Platform: Python/R
Code: https://github.com/quon-titative-biology/scAlign
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Dimensionality Reduction, Integration, Rare Cells
Added: 2019-01-25, Updated: 2025-04-12

scCAD

Cluster decomposition-based Anomaly Detection method (scCAD) is used to effectively identify rare cell types in scRNA-seq data

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

Publications
"scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data"
DOI: 10.1038/s41467-024-51891-9, Published: 2024-08-31, Citations: 9
Preprints
"Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data"
DOI: 10.1101/2024.02.25.581975, Citations: 0

Platform: Python
Code: https://github.com/xuyp-csu/scCAD
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Normalisation, Rare Cells
Added: 2024-03-08, Updated: 2025-04-13

Scedar

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

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

Publications
"Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis"
DOI: 10.1371/journal.pcbi.1007794, Published: 2020-04-27, Citations: 11
Preprints
"Scedar: a scalable Python package for single-cell RNA-seq exploratory data analysis"
DOI: 10.1101/375196, Citations: 1

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

scNovel

A neural network framework for novel rare cell deteciton

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

Publications
"scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics"
DOI: 10.1093/bib/bbae112, Published: 2024-03-28, Citations: 1

Platform: Python
Code: https://github.com/chuanyang-Zheng/scNovel
starsN/A
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last commitUnknown
License: MIT
Categories: Classification, Rare Cells
Added: 2022-10-14, Updated: 2024-04-26

scProximitE

Similarity Metrics at High Dimensionality - testing for rare cell types

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

Publications
"How does the structure of data impact cell–cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data"
DOI: 10.1093/bib/bbac387, Published: 2022-09-23, Citations: 9
Preprints
"How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data"
DOI: 10.1101/2022.04.21.489121, Citations: 0

Platform: Python
Code: https://github.com/Ebony-Watson/scProximitE
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Rare Cells, Visualisation
Added: 2022-05-20, Updated: 2022-09-30

scRAS

The goal of scRAS is to measure the cell state deviation (CSD) and cell anomalousness score (CAS) which are indicators of whether one individual cell is remote from the average expression states and whether a cell is locally anomalous, respectively
Platform: R
Code: https://github.com/AristoQian/scRAS
starsN/A
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last commitUnknown
License: MIT
Categories: Rare Cells, Visualisation
Added: 2022-12-09, Updated: 2022-12-09

ShareNet

ShareNet is a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory associations that can be used with a wide range of general network inference algorithms

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

Publications
"Bayesian information sharing enhances detection of regulatory associations in rare cell types"
DOI: 10.1093/bioinformatics/btab269, Published: 2021-07-12, Citations: 1

Platform: Python
Code: https://github.com/alexw16/sharenet
starsN/A
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last commitUnknown
License: MIT
Categories: Gene Networks, Rare Cells
Added: 2021-08-06, Updated: 2021-08-06

Spectre

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

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

Publications
"Integration, exploration, and analysis of high‐dimensional single‐cell cytometry data using Spectre"
DOI: 10.1002/cyto.a.24350, Published: 2021-04-26, Citations: 113
Preprints
"Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre"
DOI: 10.1101/2020.10.22.349563, Citations: 8

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

T

TooManyCells

Cluster single cells and analyze cell clade relationships with colorful visualizations.

Publications: 1, Preprints: 1, Total citations: 75

Publications
"TooManyCells identifies and visualizes relationships of single-cell clades"
DOI: 10.1038/s41592-020-0748-5, Published: 2020-03-02, Citations: 71
Preprints
"TooManyCells identifies and visualizes relationships of single-cell clades"
DOI: 10.1101/519660, Citations: 4

Platform: Haskell/Docker/R
Code: https://github.com/faryabiLab/too-many-cells
starsN/A
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last commitUnknown
License: GPL-3.0
Categories: Clustering, Differential Expression, Rare Cells, Visualisation
Added: 2019-01-18, Updated: 2020-05-29

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