Single-cell Software
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1
NamePlatformDOIPubDateCodeDescriptionLicense
Assembly
Alignment
UMIs
Quantification
QualityControl
Normalisation
Imputation
GeneFiltering
Clustering
Classification
Ordering
DifferentialExpression
MarkerGenes
ExpressionPatterns
VariableGenes
GeneSets
GeneNetworks
CellCycle
DimensionalityReduction
Transformation
Modality
AlternativeSplicing
RareCells
StemCells
Immune
Variants
Haplotypes
AlleleSpecific
Visualisation
Interactive
Simulation
AddedUpdated
2
AltAnalyzePython
10.1038/nature19348
2016-08-31
https://github.com/nsalomonis/altanalyze
AltAnalyze is a multi-functional and easy-to-use software package for automated single-cell and bulk gene and splicing analyses.
Apache-2.0
FALSEFALSEFALSETRUEFALSETRUEFALSETRUETRUETRUEFALSETRUETRUEFALSEFALSETRUETRUETRUETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2017-07-162017-07-21
3
anchorPython
10.1016/j.molcel.2017.06.003
2017-06-29
https://github.com/yeolab/anchor
Find bimodal, unimodal, and multimodal features in your data
BSD-3-clause
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082017-07-16
4
AUCellR
10.1101/144502
PREPRINT
https://github.com/aertslab/AUCell
AUCell is an R-package to analyze the state of gene-sets in single-cell RNA-seq data (i.e. identify cells with active gene signatures).
CustomFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-06-262017-06-26
5
BackSPINPython
10.1126/science.aaa1934
2015-03-06
https://github.com/linnarsson-lab/BackSPIN
Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.
BSD 2-clause
FALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082016-09-08
6
badgerR
https://github.com/JEFworks/badger
Bayesian approach for detecting copy number alterations from single cell RNA-seq data
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSE2017-06-052017-06-05
7
BASICPython
10.1093/bioinformatics/btw631
2016-09-28
http://ttic.uchicago.edu/~aakhan/BASIC/
BASIC is a semi-de novo assembly method to determine the full-length sequence of the BCR in single B cells from scRNA-seq data.
MITTRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSE2016-10-102017-09-09
8
BASiCSR
10.1371/journal.pcbi.1004333
2015-06-01
https://github.com/catavallejos/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.
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUE2016-09-082017-06-23
9
BEARsccR
10.1101/118919
PREPRINT
https://bitbucket.org/bsblabludwig/bearscc
BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUE2017-04-272017-04-27
10
bonvoyagePython
10.1016/j.molcel.2017.06.003
2017-06-29
https://github.com/yeolab/bonvoyage
Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
BSD-3-clause
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082017-07-16
11
BPSCR
10.1093/bioinformatics/btw202
2016-04-19
https://github.com/nghiavtr/BPSC
Beta-Poisson model for single-cell RNA-seq data analyses
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082016-09-08
12
BraCeRPython/R
10.1101/185504
PREPRINT
https://github.com/teichlab/bracer
BraCeR - reconstruction of B cell receptor sequences from single-cell RNA-seq data.
Apache-2.0
TRUETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSETRUEFALSEFALSE2017-09-092017-09-11
13
BranchedGPPython
10.1101/166868
PREPRINT
https://github.com/ManchesterBioinference/BranchedGP
BranchedGP is a package for building Branching Gaussian process models in python, using TensorFlow and GPFlow.
Apache-2.0
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-07-202017-08-20
14
BRIEPython
10.1186/s13059-017-1248-5
2017-06-27
https://github.com/huangyh09/brie
BRIE (Bayesian regression for isoform estimate) is a Bayesian method to estimate isoform proportions from RNA-seq data.
Apache-2.0
FALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-01-102017-07-03
15
BTRR
10.1186/s12859-016-1235-y
2016-09-06
https://github.com/cheeyeelim/btr
BTR is a model learning algorithm for reconstructing and training asynchronous Boolean models using single-cell expression data.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-122016-09-12
16
CellRangerPython/R
10.1038/ncomms14049
2017-01-16
Cell Ranger is a set of analysis pipelines that process Chromium single cell 3’ RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis.
FALSETRUETRUETRUETRUEFALSEFALSEFALSETRUEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2017-06-082017-09-11
17
CellityR
10.1186/s13059-016-0888-1
2016-02-17
https://github.com/teichlab/cellity
Classification of low quality cells in scRNA-seq data using R
GPL (>= 2)
FALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082016-09-08
18
CellTreeR
10.1186/s12859-016-1175-6
2016-08-13
https://github.com/Bioconductor-mirror/cellTree
Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model.
Artistic-2.0
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-082016-09-19
19
CIDRR
10.1101/068775
PREPRINT
https://github.com/VCCRI/CIDR
Ultrafast and accurate clustering through imputation and dimensionality reduction for single-cell RNA-seq data.
GPL (>=2)
FALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUE2016-09-092016-11-29
20
CitrusR
10.1101/045070
PREPRINT
https://github.com/ChenMengjie/Citrus
A normalization method to remove unwanted variation using both control and target genes.
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092017-06-05
21
clusterExperiment
R
https://github.com/epurdom/clusterExperiment
Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot.
Artistic-2.0
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082016-09-08
22
countClustR
https://github.com/kkdey/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.
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-122016-09-12
23
D3EPython
10.1186/s12859-016-0944-6
2016-02-29
https://github.com/hemberg-lab/D3E
D3E is a tool for identifying differentially-expressed genes, based on single-cell RNA-seq data. D3E consists of two modules: one for identifying differentially expressed (DE) genes, and one for fitting the parameters of a Poisson-Beta distribution.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
24
DeLoreanR
10.1093/bioinformatics/btw372
2016-06-17
https://github.com/JohnReid/DeLorean
R package to model time series accounting for noise in the temporal dimension. Specifically designed for single cell transcriptome experiments.
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-092016-09-09
25
demuxletC++
10.1101/118778
PREPRINT
https://github.com/statgen/demuxlet
Genetic multiplexing of barcoded single cell RNA-seq
Apache-2.0
FALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSE2017-03-292017-09-11
26
destinyR
10.1093/bioinformatics/btv715
2016-04-15
https://www.helmholtz-muenchen.de/icb/research/groups/quantitative-single-cell-dynamics/software/destiny/index.html
Diffusion maps for single-cell RNA-seq.
GPLFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-092017-04-18
27
DistMapR
10.1126/science.aan3235
2017-09-31
https://github.com/rajewsky-lab/distmap
DistMap can be used to spatially map single cell RNA sequencing data by using an existing reference database of in situs.
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-09-012017-09-01
28
DPTR/MATLAB
10.1038/nmeth.3971
2016-08-29
http://www.helmholtz-muenchen.de/icb/research/groups/machine-learning/projects/dpt/index.html
Our DPT software implementations make it possible to reconstruct the developmental progression of cells and identify transient or metastable states, branching decisions and differentiation endpoints.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-222017-08-01
29
DrImputeR
10.1101/181479
PREPRINT
https://github.com/ikwak2/DrImpute
DrImpute is an R package for imputing dropout events in single-cell RNA-sequencing data.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-09-012017-09-01
30
dropbeadR
10.1101/099473
PREPRINT
https://github.com/rajewsky-lab/dropbead
It offers a quick and straightfoward way to explore and perform basic analysis of single cell sequencing data coming from droplet sequencing, such as Drop-seq.
GPL-3FALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-01-162017-01-16
31
dropClust
R/C++/Python
10.1101/170308
PREPRINT
https://github.com/debsin/dropClust
Efficient clustering of ultra-large scRNA-seq data
GPL (>= 3)
FALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-08-202017-08-20
32
dropEstC++/R
10.1101/171496
PREPRINT
https://github.com/hms-dbmi/dropEst
Pipeline for estimating molecular count matrices for droplet-based single-cell RNA-seq measurements.
GPL-3FALSEFALSETRUETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-08-202017-09-11
33
dropSeqPipe
Snakemake
https://github.com/Hoohm/dropSeqPipe
This pipeline is based on snakemake and the dropseq tools provided by the McCarroll Lab. It allows to go from raw data of your dropSeq/scrbSeq experiment until the final count matrix with QC plots along the way.
CC BY-SA 4.0
FALSETRUETRUETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-05-212017-09-11
34
DrSeq2Python
10.1371/journal.pone.0180583
2017-07-03
https://github.com/ChengchenZhao/DrSeq2
Quality control and analysis pipeline for parallel single cell transcriptome and epigenome data
GPL-3FALSETRUETRUETRUETRUETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-06-262017-09-18
35
DTWScoreR
10.1186/s12859-017-1647-3
2017-05-23
https://github.com/xiaoxiaoxier/DTWscore
Transcriptional heterogeneity analysis for time-series single-cell RNA-seq data
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-05-302017-05-30
36
ECLAIRPython
10.1101/036533
PREPRINT
https://github.com/GGiecold/ECLAIR
ECLAIR stands for Ensemble Clustering for Lineage Analysis, Inference and Robustness. Robust and scalable inference of cell lineages from gene expression data.
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-082016-09-08
37
embeddrR
10.1101/027219
PREPRINT
https://github.com/kieranrcampbell/embeddr
Embeddr creates a reduced dimensional representation of the gene space using a high-variance gene correlation graph and laplacian eigenmaps. It then fits a smooth pseudotime trajectory using principal curves.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-122016-09-12
38
ESATJava
10.1101/gr.207902.116
2016-07-28
https://github.com/garber-lab/ESAT
The ESAT toolkit is designed for expression analysis of Digital expression (DGE) libraries that target transcript "ends". ESAT takes a set of alignment files (SAM or BAM) with genome alignment coordinates, a file containing transcript coordinates (BED or text file) and outputs read counts for each transcript provided.FALSEFALSEFALSETRUEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
39
f-scLVMPython
10.1101/087775
PREPRINT
https://github.com/PMBio/f-scLVM
f-scLVM is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors.
Apache-2.0
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-11-292016-11-29
40
FalcoAWS
10.1101/064006
PREPRINT
https://github.com/VCCRI/Falco
A Cloud-based Genetic Feature Quantification Analysis Tool
GPL-3FALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082017-09-11
41
FastProjectPython
10.1186/s12859-016-1176-5
2016-08-23
https://github.com/YosefLab/FastProject
Here we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.
CustomFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2016-09-092016-09-09
42
flotillaPython
https://github.com/yeolab/flotilla
Reproducible machine learning analysis of gene expression and alternative splicing data
CustomFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSE2016-09-082016-09-08
43
GiniClustR/Python
10.1186/s13059-016-1010-4
2016-07-01
https://github.com/lanjiangboston/GiniClust
GiniClust is a clustering method implemented in Python and R for detecting rare cell-types from large-scale single-cell gene expression data.
MITFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSE2016-09-092016-09-09
44
GPfatesPython
10.1126/sciimmunol.aal2192
2017-03-03
https://github.com/Teichlab/GPfates
Model transcriptional cell fates as mixtures of Gaussian Processes
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-192017-03-10
45
GranatumVirtualbox
10.1101/110759
PREPRINT
https://gitlab.com/uhcclxgg/granatum
This is a graphical single-cell RNA-seq (scRNA-seq) analysis pipeline for genomics scientists.FALSEFALSEFALSEFALSETRUETRUEFALSETRUETRUEFALSETRUETRUEFALSEFALSEFALSETRUETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2017-03-012017-03-01
46
GRMR
10.1093/bioinformatics/btv122
2015-07-01
http://wanglab.ucsd.edu/star/GRM
Normalization and noise reduction for singlecell RNA-seq experimentsFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092017-04-18
47
HocusPocusR
https://github.com/joeburns06/hocuspocus
Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data.FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-082016-09-08
48
HopLandMATLAB
10.1093/bioinformatics/btx232
2017-07-12
https://github.com/NetLand-NTU/HopLand
Single-cell pseudotime recovery using continuous Hopfield network based modeling of Waddington’s epigenetic landscape
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-07-242017-07-24
49
ISOPR
10.1101/036988
PREPRINT
https://github.com/nghiavtr/ISOP
ISOform-level expression Patterns in single-cell RNA-sequencing data.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
50
k-branchesR
10.1101/094532
PREPRINT
https://github.com/theislab/kbranches
The main idea behind the K-Branches method is to identify regions of interest (branching regions and tips) in differentiation trajectories of single cells.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-12-222016-12-22
51
LEAPR
10.1093/bioinformatics/btw729
2016-12-19
https://cran.r-project.org/web/packages/LEAP/index.html
Constructing Gene Co-Expression Networks for Single-Cell RNA-Sequencing Data Using Pseudotime Ordering.
GPL-2FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-122017-04-18
52
LinnormR
https://github.com/Bioconductor-mirror/Linnorm
Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data.
MITFALSEFALSEFALSEFALSEFALSETRUETRUETRUETRUEFALSEFALSETRUEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUE2017-02-012017-02-01
53
M3DropR
10.1101/065094
PREPRINT
https://github.com/tallulandrews/M3Drop
R package providing functions for fitting a modified Michaelis-Menten (MM) equation to the pattern of dropouts observed in a single-cell sequencing experiment.
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-10-102016-10-10
54
MAGICPython
10.1101/111591
PREPRINT
https://github.com/pkathail/magic
Markov Affinity-based Graph Imputation of Cells (MAGIC)
GPL-2FALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2017-02-272017-02-17
55
MASTR
10.1186/s13059-015-0844-5
2015-12-10
https://github.com/RGLab/MAST
Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data.
GPL (>= 2)
FALSEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082016-09-08
56
MATCHERPython
10.1186/s13059-017-1269-0
2017-07-2017
https://github.com/jw156605/MATCHER
Manifold Alignment to Characterize Experimental Relationships (MATCHER)
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-08-252017-08-25
57
MetaNeighbour
R
10.1101/150524
PREPRINT
https://github.com/maggiecrow/MetaNeighbor
MetaNeighbor: a method to rapidly assess cell type identity using both functional and random gene sets
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-09-092017-09-09
58
MFAR
10.12688/wellcomeopenres.11087.1
2017-03-15
https://github.com/kieranrcampbell/mfa
mfa is an R package implementing Gibbs sampling for a Bayesian hierarchichal mixture of factor analysers for inference of bifurcations in single-cell data.
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUE2017-03-202017-03-20
59
MonocleR
10.1038/nbt.2859
2014-04-01
https://github.com/cole-trapnell-lab/monocle-release
Differential expression and time-series analysis for single-cell RNA-Seq.
Artistic-2.0
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUETRUETRUETRUEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-082017-07-24
60
MpathR
10.1038/ncomms11988
2016-06-30
https://github.com/JinmiaoChenLab/Mpath
Mpath: an algorithm for constructing multi-branching cell lineages from single-cell dataFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
61
OEFinderR
10.1093/bioinformatics/btw004
2016-01-06
https://github.com/lengning/OEFinder
Identify ordering effect genes in single cell RNA-seq data. OEFinder shiny impelemention depends on packages shiny, shinyFiles, gdata, and EBSeq.FALSEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSE2016-09-082016-09-08
62
OscopeR
10.1038/nmeth.3549
2015-08-24
https://github.com/Bioconductor-mirror/Oscope
Oscope: a statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq experiments.
Artistic-2.0
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
63
OuijaR
10.1101/060442
PREPRINT
https://github.com/kieranrcampbell/ouija
Incorporate prior information into single-cell trajectory (pseudotime) analyses using Bayesian nonlinear factor analysis.
GPL (>= 3)
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-082016-09-13
64
OutriggerPython
10.1016/j.molcel.2017.06.003
2017-06-29
https://github.com/YeoLab/outrigger
Outrigger is a program to calculate alternative splicing scores of RNA-Seq data based on junction reads and a de novo, custom annotation created with a graph database, especially made for single-cell analyses.
BSD-3-clause
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082017-07-16
65
PBAPython
10.1101/170118
PREPRINT
https://github.com/AllonKleinLab/PBA
Population balance analysis (PBA) relates the observed states of a system to its steady-state dynamics by applying the law of population balance.
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-08-072017-08-07
66
pcaReduceR
10.1186/s12859-016-0984-y
2016-03-22
https://github.com/JustinaZ/pcaReduce
Hierarchical clustering of single cell transcriptional profiles
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
67
PhenoPathR
10.1101/159913
PREPRINT
https://github.com/kieranrcampbell/phenopath
PhenoPath learns genomic trajectories (pseudotimes) in the presence of heterogenous environmental and genetic backgrounds encoded as additional covariates and identifies interactions between the trajectories and covariates.
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUE2017-07-162017-07-16
68
PIVOTR
10.1101/053348
PREPRINT
https://github.com/qinzhu/PIVOT
PIVOT facilitates fast and interactive RNA-Seq data analysis and visualization.
CustomFALSEFALSEFALSEFALSETRUETRUEFALSETRUETRUEFALSETRUETRUEFALSEFALSEFALSETRUETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2017-07-162017-07-16
69
PoissonUMIsR
https://github.com/tallulandrews/PoissonUMIs
R package providing functions for fitting, analyzing and visualizing single-cell RNASeq data which has been quantified by counting UMIs while accounting for different sequencing depths/detection rates between cells.
GPL-2FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-10-102016-10-10
70
powsimRR
10.1101/117150
PREPRINT
https://github.com/bvieth/powsimR
Power analysis for bulk and single cell RNA-seq experiments
GPLFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUE2017-03-202017-06-30
71
pseudogpR
10.1101/047365
PREPRINT
https://github.com/kieranrcampbell/pseudogp
pseudogp is an R package for Bayesian inference of Gaussian Process Latent Variable models learning pseudotimes from single-cell RNA-seq.
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-092016-09-09
72
RaceID2R
10.1016/j.stem.2016.05.010
2016-06-21
https://github.com/dgrun/StemID
RaceID2 is an advanced version of RaceID, an algorithm for the identification of rare and abundant cell types from single cell transcriptome data.FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
73
RCAR
10.1038/ng.3818
2017-03-20
https://github.com/GIS-SP-Group/RCA
RCA, short for Reference Component Analysis, is an R package for robust clustering analysis of single cell RNA sequencing data (scRNAseq).
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-03-272017-03-27
74
reCATR
10.1038/s41467-017-00039-z
2017-06-19
https://github.com/tinglab/reCAT
reCAT is a modelling framework for unsynchronized single-cell transcriptome data that can reconstruct a high-resolution cell cycle time-series.
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-07-162017-07-16
75
sakeR
https://github.com/naikai/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.
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSETRUEFALSETRUETRUEFALSEFALSETRUEFALSEFALSEFALSETRUETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2016-10-102016-10-10
76
SAMstrtR
10.1093/bioinformatics/btt511
2013-11-15
https://github.com/shka/R-SAMstrt
Statistical test for differential expression in single-celltranscriptome with spike-in normalization
LGLP-3.0FALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-092016-09-09
77
SAVERR
10.1101/138677
PREPRINT
https://github.com/mohuangx/SAVER
SAVER (Single-cell Analysis Via Expression Recovery) implements a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data.
GPL-2FALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-06-232017-06-23
78
SC3R
10.1101/036558
PREPRINT
https://github.com/hemberg-lab/sc3
SC3 is an interactive tool for the unsupervised clustering of cells from single cell RNA-Seq experiments.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSE2016-09-082016-09-08
79
SCALER
10.1186/s13059-017-1200-8
2017-04-26
https://github.com/yuchaojiang/SCALE
Allele-Specific Expression by Single-Cell RNA Sequencing
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSE2017-02-232017-04-05
80
ScanpyPython
10.1101/174029
PREPRINT
https://github.com/theislab/scanpy
Single-Cell Analysis in Python
BSD-3-clause
FALSEFALSEFALSEFALSEFALSETRUEFALSETRUETRUEFALSETRUETRUEFALSEFALSETRUEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUE2017-05-212017-09-18
81
ScaterR
10.1093/bioinformatics/btw777
2017-01-14
https://github.com/davismcc/scater
Scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis, filling a useful niche between raw RNA-sequencing count or transcripts-per-million data and more focused downstream modelling tools such as monocle, scLVM, SCDE, edgeR, limma and so on.
GPL (>= 2)
FALSEFALSEFALSETRUETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2016-09-082017-03-01
82
scDDR
10.1186/s13059-016-1077-y
2016-10-25
https://github.com/kdkorthauer/scDD
scDD (Single-Cell Differential Distributions) is a framework to identify genes with different expression patterns between biological groups of interest. In addition to traditional differential expression, it can detect differences that are more complex and subtle than a mean shift.
GPL-2FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUE2016-09-082016-11-07
83
SCDER
10.1038/nmeth.2967
2014-07-01
https://github.com/hms-dbmi/scde
Differential expression using error models and overdispersion-based identification of important gene sets.FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-082016-09-08
84
SCellMATLAB
10.1093/bioinformatics/btw201
2016-04-19
https://github.com/diazlab/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.
GPLFALSEFALSEFALSEFALSETRUETRUEFALSETRUETRUEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2016-09-082016-09-08
85
SCENICR
10.1101/144501
PREPRINT
https://github.com/aertslab/SCENIC
SCENIC is a tool to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
CustomFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2017-06-262017-06-26
86
SCENTR
10.1038/ncomms15599
2017-06-01
https://github.com/aet21/SCENT
scent_1.0 is an R-package for analysis of single-cell RNA-Seq data. It uses single-cell entropy to help analyse and interpret such data.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-11-092017-06-08
87
SCIMITARPython
10.1101/070151
PREPRINT
https://github.com/dimenwarper/scimitar
SCIMITAR provides a variety of tools to analyze trajectory maps of single-cell measurements.FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-122016-09-12
88
scImputeR
10.1101/141598
PREPRINT
https://github.com/Vivianstats/scImpute
scImpute is developed to accurately and robustly impute the dropout values in scRNA-seq data
FALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-05-302017-05-30
89
scLVMR/Python
10.1038/nbt.3102
2015-02-01
https://github.com/PMBio/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.
Apache-2.0
FALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-09-082016-12-08
90
scmapR
10.1101/150292
PREPRINT
https://github.com/hemberg-lab/scmap
scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types identified in a different experiment.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-06-232017-06-23
91
SCNormR
10.1038/nmeth.4263
2017-04-17
https://github.com/rhondabacher/SCnorm
A quantile regression based approach for robust normalization of single cell RNA-seq data.
GPL (>= 2)
FALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-11-292017-04-27
92
SCODER
10.1101/088856
PREPRINT
https://github.com/hmatsu1226/SCODE
SCODE : an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-11-292016-11-29
93
SCONER
https://github.com/YosefLab/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.
Artistic-2.0
FALSEFALSEFALSEFALSETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUETRUEFALSE2016-09-082016-09-08
94
SCORPIUSR
10.1101/079509
PREPRINT
https://github.com/rcannood/SCORPIUS
SCORPIUS an unsupervised approach for inferring developmental chronologies from single-cell RNA sequencing data.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSE2016-10-112016-10-11
95
SCOUPC++
https://github.com/hmatsu1226/SCOUP
Uses probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
MITFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082016-09-08
96
SCPatternR
10.1101/046110
PREPRINT
https://github.com/lengning/SCPattern
A statistical approach to identify and classify expression changes in single cell RNA-seq experiments with ordered conditions.
Apache-2.0
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSE2016-09-092016-09-09
97
scphaserR
10.1093/bioinformatics/btw484
2016-08-06
https://github.com/edsgard/scphaser
scphaser is an R package for haplotype phasing using single-cell RNA-seq data.
GPL-3FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSE2016-09-092016-09-09
98
scPipeR
10.1101/175927
PREPRINT
https://github.com/LuyiTian/scPipe
a pipeline for single cell RNA-seq data analysis
GPL (>= 2)
FALSETRUETRUETRUETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUE2017-06-302017-09-18
99
scranR
10.1186/s13059-016-0947-7
2016-04-27
https://github.com/MarioniLab/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.
GPL-3FALSEFALSEFALSEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2016-09-082016-09-08
100
SCRLC++
10.1093/nar/gkx750
2017-08-28
https://github.com/SuntreeLi/SCRL
This is the SCRL toolkit developed for learning meaningful representations for scRNA-seq data by integrating multiple source of network information.
FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSETRUEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE2017-09-092017-09-09
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