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single-cell-software.json
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[
{
"Name": "BASIC",
"Platform": "Python",
"DOI": "10.1093/bioinformatics/btw631",
"Pub Date": "2016-09-28",
"Code": "http://ttic.uchicago.edu/~aakhan/BASIC/",
"Description": "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.",
"License": "MIT",
"Added": "2016-10-10",
"Updated": "2016-10-10",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw631",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["Assembly"]
},
{
"Name": "BRIE",
"Platform": "Python",
"DOI": "10.1101/098517",
"Code": "https://github.com/huangyh09/brie",
"Description": "BRIE (Bayesian regression for isoform estimate) is a Bayesian method to estimate isoform proportions from RNA-seq data.",
"License": "Apache-2.0",
"Added": "2017-01-10",
"Updated": "2017-01-10",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/098517",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "Alternative Splicing"]
},
{
"Name": "demuxlet",
"Platform": "C++",
"DOI": "10.1101/118778",
"Code": "https://github.com/hyunminkang/apigenome",
"Description": "Sample demultiplexing of droplet sc-RNAseq reads.",
"License": "GPL-3",
"Added": "2017-03-29",
"Updated": "2017-03-29",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/118778",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "ESAT",
"Platform": "Java",
"DOI": "10.1101/gr.207902.116",
"Pub Date": "2016-07-28",
"Code": "https://github.com/garber-lab/ESAT",
"Description": "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.",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1101/gr.207902.116",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "Clustering", "Pseudotime", "Dimensionality Reduction"]
},
{
"Name": "Falco",
"Platform": "AWS",
"DOI": "10.1101/064006",
"Code": "https://github.com/VCCRI/Falco/",
"Description": "A Cloud-based Genetic Feature Quantification Analysis Tool",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/064006",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "Scater",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw777",
"Pub Date": "2017-01-14",
"Code": "https://github.com/davismcc/scater",
"Description": "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.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2017-03-01",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw777",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "QC", "Normalisation", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "TASC",
"Platform": "C++",
"DOI": "10.1101/116939",
"Code": "https://github.com/scrna-seq/TASC",
"Description": "TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins.",
"License": "MIT",
"Added": "2017-03-29",
"Updated": "2017-03-29",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/116939",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "Normalisation", "DE"]
},
{
"Name": "UMI-tools",
"Platform": "Python",
"DOI": "10.1101/051755",
"Code": "https://github.com/CGATOxford/UMI-tools",
"Description": "This repository contains tools for dealing with Unique Molecular Identifiers (UMIs)/Random Molecular Tags (RMTs).",
"License": "MIT",
"Added": "2016-09-12",
"Updated": "2016-09-12",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/051755",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "umis",
"Platform": "Python",
"DOI": "10.1038/nmeth.4220",
"Pub Date": "2017-03-06",
"Code": "https://github.com/vals/umis",
"Description": "umis provides tools for estimating expression in RNA-Seq data which performs sequencing of end tags of trancsript, and incorporate molecular tags to correct for amplification bias.",
"License": "MIT",
"Added": "2016-09-12",
"Updated": "2017-03-10",
"DOI_url": "http://dx.doi.org/10.1038/nmeth.4220",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "Cellity",
"Platform": "R",
"DOI": "10.1186/s13059-016-0888-1",
"Pub Date": "2016-02-17",
"Code": "https://github.com/teichlab/cellity",
"Description": "Classification of low quality cells in scRNA-seq data using R",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1186/s13059-016-0888-1",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC"]
},
{
"Name": "dropbead",
"Platform": "R",
"DOI": "10.1101/099473",
"Code": "https://github.com/rajewsky-lab/dropbead",
"Description": "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.",
"License": "GPL-3",
"Added": "2017-01-16",
"Updated": "2017-01-16",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/099473",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Visualisation"]
},
{
"Name": "flotilla",
"Platform": "Python",
"Code": "https://github.com/yeolab/flotilla",
"Description": "Reproducible machine learning analysis of gene expression and alternative splicing data",
"License": "Custom",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Clustering", "Gene Networks", "Dimensionality Reduction", "Interactive"]
},
{
"Name": "Granatum",
"Platform": "Virtualbox",
"DOI": "10.1101/110759",
"Code": "https://gitlab.com/uhcclxgg/granatum",
"Description": "This is a graphical single-cell RNA-seq (scRNA-seq) analysis pipeline for genomics scientists.",
"Added": "2017-03-01",
"Updated": "2017-03-01",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/110759",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Gene Filtering", "Clustering", "Pseudotime", "DE", "Gene Sets", "Gene Networks", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "MAGIC",
"Platform": "Python",
"DOI": "10.1101/111591",
"Code": "https://github.com/pkathail/magic/",
"Description": "Markov Affinity-based Graph Imputation of Cells (MAGIC)",
"License": "GPL-2",
"Added": "2017-02-27",
"Updated": "2017-02-17",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/111591",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Imputation", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "MAST",
"Platform": "R",
"DOI": "10.1186/s13059-015-0844-5",
"Pub Date": "2015-12-10",
"Code": "https://github.com/RGLab/MAST",
"Description": "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.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1186/s13059-015-0844-5",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "DE", "Gene Sets", "Gene Networks"]
},
{
"Name": "OEFinder",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw004",
"Pub Date": "2016-01-06",
"Code": "https://github.com/lengning/OEFinder",
"Description": "Identify ordering effect genes in single cell RNA-seq data. OEFinder shiny impelemention depends on packages shiny, shinyFiles, gdata, and EBSeq.",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw004",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Interactive"]
},
{
"Name": "SCell",
"Platform": "Matlab",
"DOI": "10.1093/bioinformatics/btw201",
"Pub Date": "2016-04-19",
"Code": "https://github.com/diazlab/SCell",
"Description": "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.",
"License": "GNU",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw201",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Gene Filtering", "Clustering", "Pseudotime", "DE", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "SCONE",
"Platform": "R",
"Code": "https://github.com/YosefLab/scone",
"Description": "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.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Visualisation", "Interactive"]
},
{
"Name": "scTDA",
"Platform": "Python",
"DOI": "10.1038/nbt.3854",
"Pub Date": "2017-05-01",
"Code": "https://github.com/RabadanLab/scTDA",
"Description": "scTDA is an object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data. It includes tools for the preprocessing, analysis, and exploration of single-cell RNA-seq data based on topological representations.",
"License": "GPL-3",
"Added": "2017-05-09",
"Updated": "2017-05-09",
"DOI_url": "http://dx.doi.org/10.1038/nbt.3854",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Pseudotime", "Visualisation"]
},
{
"Name": "Sincera",
"Platform": "R",
"DOI": "10.1371/journal.pcbi.1004575",
"Pub Date": "2015-11-04",
"Code": "https://research.cchmc.org/pbge/sincera.html",
"Description": "R-based pipeline for single-cell analysis including clustering and visualization.",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1371/journal.pcbi.1004575",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Gene Filtering", "Clustering", "DE", "Marker Genes"]
},
{
"Name": "SinQC",
"Platform": "R/Python",
"DOI": "10.1093/bioinformatics/btw176",
"Pub Date": "2016-04-10",
"Code": "http://www.morgridge.net/SinQC.html",
"Description": "A Method and Tool to Control Single-cell RNA-seq Data Quality.",
"Added": "2016-09-08",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw176",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC"]
},
{
"Name": "BASiCs",
"Platform": "R",
"DOI": "10.1371/journal.pcbi.1004333",
"Pub Date": "2015-06-01",
"Code": "https://github.com/catavallejos/BASiCS",
"Description": "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.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-13",
"DOI_url": "http://dx.doi.org/10.1371/journal.pcbi.1004333",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "DE", "Variable Genes"]
},
{
"Name": "Citrus (scPLS)",
"Platform": "R",
"DOI": "10.1101/045070",
"Code": "https://github.com/ChenMengjie/Citrus",
"Description": "A normalization method to remove unwanted variation using both control and target genes.",
"License": "GPL (>=2)",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/045070",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation"]
},
{
"Name": "GiniClust",
"Platform": "R/Python",
"DOI": "10.1186/s13059-016-1010-4",
"Pub Date": "2016-07-01",
"Code": "https://github.com/lanjiangboston/GiniClust",
"Description": "GiniClust is a clustering method implemented in Python and R for detecting rare cell-types from large-scale single-cell gene expression data.",
"License": "MIT",
"Added": "2016-09-09",
"Updated": "2016-09-09",
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"DOI": "10.1093/bioinformatics/btv122",
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"Description": "Normalization and noise reduction for singlecell RNA-seq experiments",
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{
"Name": "Linnorm",
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"Description": "Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data.",
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"Name": "sake",
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"Code": "https://github.com/naikai/sake",
"Description": "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.",
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{
"Name": "SAMstrt",
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"DOI": "10.1093/bioinformatics/btt511",
"Pub Date": "2013-11-15",
"Code": "https://github.com/shka/R-SAMstrt",
"Description": "Statistical test for differential expression in single-celltranscriptome with spike-in normalization",
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"DOI": "10.1038/nbt.3102",
"Pub Date": "2015-02-01",
"Code": "https://github.com/PMBio/scLVM",
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"DOI": "10.1038/nmeth.4263",
"Pub Date": "2017-04-17",
"Code": "https://github.com/rhondabacher/SCnorm",
"Description": "A quantile regression based approach for robust normalization of single cell RNA-seq data.",
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"DOI_url": "http://dx.doi.org/10.1038/nmeth.4263",
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{
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"DOI": "10.1186/s13059-016-0947-7",
"Pub Date": "2016-04-27",
"Code": "https://github.com/MarioniLab/scran",
"Description": "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.",
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{
"Name": "Seurat",
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"DOI": "10.1038/nbt.3192",
"Pub Date": "2015-04-13",
"Code": "https://github.com/satijalab/seurat",
"Description": "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.",
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"Name": "ZINB-WaVE",
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"DOI": "10.1101/125112",
"Code": "https://github.com/drisso/zinbwave",
"Description": "Zero-inflated Negative Binomial based Wanted Variation Extraction (ZINB-WaVE)",
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"Name": "CIDR",
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"DOI": "10.1101/068775",
"Code": "https://github.com/VCCRI/CIDR",
"Description": "Ultrafast and accurate clustering through imputation and dimensionality reduction for single-cell RNA-seq data.",
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"Added": "2016-09-09",
"Updated": "2016-11-29",
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},
{
"Name": "BackSPIN",
"Platform": "Python",
"DOI": "10.1126/science.aaa1934",
"Pub Date": "2015-03-06",
"Code": "https://github.com/linnarsson-lab/BackSPIN",
"Description": "Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.",
"License": "BSD 2-clause",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1126/science.aaa1934",
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"categories": ["Gene Filtering", "Clustering"]
},
{
"Name": "DeLorean",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw372",
"Pub Date": "2016-06-17",
"Code": "https://github.com/JohnReid/DeLorean",
"Description": "R package to model time series accounting for noise in the temporal dimension. Specifically designed for single cell transcriptome experiments.",
"License": "MIT",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw372",
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"categories": ["Gene Filtering", "Pseudotime", "Expression Patterns", "Visualisation"]
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{
"Name": "BEARscc",
"Platform": "R",
"DOI": "10.1101/118919",
"Code": "https://bitbucket.org/bsblabludwig/bearscc",
"Description": "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.",
"License": "GPL-3",
"Added": "2017-04-27",
"Updated": "2017-04-27",
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},
{
"Name": "clusterExperiment",
"Platform": "R",
"Code": "https://github.com/epurdom/clusterExperiment",
"Description": "Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-08",
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"categories": ["Clustering"]
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{
"Name": "countClust",
"Platform": "R",
"Code": "https://github.com/kkdey/CountClust",
"Description": "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.",
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"Updated": "2016-09-12",
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"categories": ["Clustering", "DE", "Variable Genes", "Visualisation"]
},
{
"Name": "ECLAIR",
"Platform": "Python",
"DOI": "10.1101/036533",
"Code": "https://github.com/GGiecold/ECLAIR",
"Description": "ECLAIR stands for Ensemble Clustering for Lineage Analysis, Inference and Robustness. Robust and scalable inference of cell lineages from gene expression data.",
"License": "MIT",
"Added": "2016-09-08",
"Updated": "2016-09-08",
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},
{
"Name": "pcaReduce",
"Platform": "R",
"DOI": "10.1186/s12859-016-0984-y",
"Pub Date": "2016-03-22",
"Code": "https://github.com/JustinaZ/pcaReduce",
"Description": "Hierarchical clustering of single cell transcriptional profiles",
"License": "GPL (>= 2)",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1186/s12859-016-0984-y",
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"categories": ["Clustering"]
},
{
"Name": "RCA",
"Platform": "R",
"DOI": "10.1038/ng.3818",
"Pub Date": "2017-03-20",
"Code": "https://github.com/GIS-SP-Group/RCA",
"Description": "RCA, short for Reference Component Analysis, is an R package for robust clustering analysis of single cell RNA sequencing data (scRNAseq).",
"License": "MIT",
"Added": "2017-03-27",
"Updated": "2017-03-27",
"DOI_url": "http://dx.doi.org/10.1038/ng.3818",
"Github": true,
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"categories": ["Clustering", "Visualisation"]
},
{
"Name": "SC3",
"Platform": "R",
"DOI": "10.1101/036558",
"Code": "https://github.com/hemberg-lab/sc3",
"Description": "SC3 is an interactive tool for the unsupervised clustering of cells from single cell RNA-Seq experiments.",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/036558",
"Github": true,
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"CRAN": false,
"categories": ["Clustering", "Interactive"]
},
{
"Name": "SIMLR",
"Platform": "R",
"DOI": "10.1038/nmeth.4207",
"Pub Date": "2017-03-06",
"Code": "https://github.com/BatzoglouLabSU/SIMLR",
"Description": "Single-cell Interpretation via Multi-kernel LeaRning which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.",
"License": "GPL-3",
"Added": "2016-09-09",
"Updated": "2017-03-10",
"DOI_url": "http://dx.doi.org/10.1038/nmeth.4207",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "sincell",
"Platform": "R",
"DOI": "10.1101/014472",
"Code": "https://github.com/Bioconductor-mirror/sincell",
"Description": "Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/014472",
"Github": true,
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"CRAN": false,
"categories": ["Clustering", "Pseudotime", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "SPADE",
"Platform": "R",
"DOI": "10.1038/nprot.2016.066",
"Pub Date": "2016-06-16",
"Code": "https://github.com/nolanlab/spade",
"Description": "Visualization and cellular hierarchy inference of single-cell data using SPADE.",
"License": "GPL (>=2)",
"Added": "2016-09-09",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1038/nprot.2016.066",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Pseudotime", "Marker Genes", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "SSrGE",
"Platform": "Python",
"DOI": "10.1101/095810",
"Code": "https://github.com/lanagarmire/SSrGE",
"Description": "This procedure aims to fit sparse linear models using a binary matrix (n_samples x n_SNV) as features matrix and a gene expression matrix (n_genes x n_samples) as response.",
"License": "MIT",
"Added": "2017-01-10",
"Updated": "2017-01-10",
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"DOI_url": "http://dx.doi.org/10.1101/095810",
"Github": true,
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"categories": ["Clustering", "Variants"]
},
{
"Name": "TSCAN",
"Platform": "R",
"DOI": "10.1093/nar/gkw430",
"Pub Date": "2016-05-13",
"Code": "https://github.com/zji90/TSCAN",
"Description": "Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.",
"License": "GPL (>=2)",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1093/nar/gkw430",
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"categories": ["Clustering", "Pseudotime", "Marker Genes", "Visualisation", "Interactive"]
},
{
"Name": "CellTree",
"Platform": "R",
"DOI": "10.1186/s12859-016-1175-6",
"Pub Date": "2016-08-13",
"Code": "https://github.com/Bioconductor-mirror/cellTree",
"Description": "Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-19",
"DOI_url": "http://dx.doi.org/10.1186/s12859-016-1175-6",
"Github": true,
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"CRAN": false,
"categories": ["Pseudotime", "Gene Sets", "Visualisation"]
},
{
"Name": "DPT",
"Platform": "R/Matlab",
"DOI": "10.1038/nmeth.3971",
"Pub Date": "2019-08-29",
"Code": "http://www.helmholtz-muenchen.de/icb/research/groups/machine-learning/projects/dpt/index.html",
"Description": "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.",
"License": "GPL-3",
"Added": "2016-09-22",
"Updated": "2016-09-22",
"DOI_url": "http://dx.doi.org/10.1038/nmeth.3971",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Visualisation"]
},
{
"Name": "embeddr",
"Platform": "R",
"DOI": "10.1101/027219",
"Code": "https://github.com/kieranrcampbell/embeddr",
"Description": "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.",
"License": "GPL-3",
"Added": "2016-09-12",
"Updated": "2016-09-12",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/027219",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "GPfates",
"Platform": "Python",
"DOI": "10.1126/sciimmunol.aal2192",
"Pub Date": "2017-03-03",
"Code": "https://github.com/Teichlab/GPfates",
"Description": "Model transcriptional cell fates as mixtures of Gaussian Processes",
"License": "MIT",
"Added": "2016-09-19",
"Updated": "2017-03-10",
"DOI_url": "http://dx.doi.org/10.1126/sciimmunol.aal2192",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Visualisation"]
},
{
"Name": "k-branches",
"Platform": "R",
"DOI": "10.1101/094532",
"Code": "https://github.com/theislab/kbranches",
"Description": "The main idea behind the K-Branches method is to identify regions of interest (branching regions and tips) in differentiation trajectories of single cells.",
"License": "GPL-3",
"Added": "2016-12-22",
"Updated": "2016-12-22",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/094532",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "LEAP",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw729",
"Pub Date": "2016-12-19",
"Code": "https://cran.r-project.org/web/packages/LEAP/index.html",
"Description": "Constructing Gene Co-Expression Networks for Single-Cell RNA-Sequencing Data Using Pseudotime Ordering.",
"License": "GPL-2",
"Added": "2016-09-12",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw729",
"Github": false,
"Bioconductor": false,
"CRAN": true,
"categories": ["Pseudotime", "Gene Networks"]
},
{
"Name": "MFA",
"Platform": "R",
"DOI": "10.12688/wellcomeopenres.11087.1",
"Pub Date": "2017-03-15",
"Code": "https://github.com/kieranrcampbell/mfa",
"Description": "mfa is an R package implementing Gibbs sampling for a Bayesian hierarchichal mixture of factor analysers for inference of bifurcations in single-cell data.",
"License": "GPL (>= 2)",
"Added": "2017-03-20",
"Updated": "2017-03-20",
"DOI_url": "http://dx.doi.org/10.12688/wellcomeopenres.11087.1",
"Github": true,
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"CRAN": false,
"categories": ["Pseudotime", "Visualisation", "Simulation"]
},
{
"Name": "Monocle",
"Platform": "R",
"DOI": "10.1038/nbt.2859",
"Pub Date": "2014-04-01",
"Code": "https://github.com/cole-trapnell-lab/monocle-release",
"Description": "Differential expression and time-series analysis for single-cell RNA-Seq.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1038/nbt.2859",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "DE", "Expression Patterns", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "Mpath",
"Platform": "R",
"DOI": "10.1038/ncomms11988",
"Pub Date": "2016-06-30",
"Code": "https://github.com/JinmiaoChenLab/Mpath",
"Description": "Mpath: an algorithm for constructing multi-branching cell lineages from single-cell data",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1038/ncomms11988",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "Ouija",
"Platform": "R",
"DOI": "10.1101/060442",
"Code": "https://github.com/kieranrcampbell/ouija",
"Description": "Incorporate prior information into single-cell trajectory (pseudotime) analyses using Bayesian nonlinear factor analysis.",
"License": "GPL (>=3)",
"Added": "2016-09-08",
"Updated": "2016-09-13",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/060442",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Visualisation"]
},
{
"Name": "pseudogp",
"Platform": "R",
"DOI": "10.1101/047365",
"Code": "https://github.com/kieranrcampbell/pseudogp",
"Description": "pseudogp is an R package for Bayesian inference of Gaussian Process Latent Variable models learning pseudotimes from single-cell RNA-seq.",
"License": "MIT",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/047365",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Visualisation"]
},
{
"Name": "SCENT",
"Platform": "R",
"DOI": "10.1101/084202",
"Code": "https://github.com/aet21/SCENT",
"Description": "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.",
"License": "GPL-3",
"Added": "2016-11-09",
"Updated": "2017-04-18",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/084202",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "SCIMITAR",
"Platform": "Python",
"DOI": "10.1101/070151",
"Code": "https://github.com/dimenwarper/scimitar",
"Description": "SCIMITAR provides a variety of tools to analyze trajectory maps of single-cell measurements.",
"Added": "2016-09-12",
"Updated": "2016-09-12",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/070151",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Gene Networks"]
},
{
"Name": "SCODE",
"Platform": "R",
"DOI": "10.1101/088856",
"Code": "https://github.com/hmatsu1226/SCODE",
"Description": "SCODE : an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.",
"License": "MIT",
"Added": "2016-11-29",
"Updated": "2016-11-29",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/088856",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Gene Networks"]
},
{
"Name": "SCORPIUS",
"Platform": "R",
"DOI": "10.1101/079509",
"Code": "https://github.com/rcannood/SCORPIUS",
"Description": "SCORPIUS an unsupervised approach for inferring developmental chronologies from single-cell RNA sequencing data.",
"License": "GPL-3",
"Added": "2016-10-11",
"Updated": "2016-10-11",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/079509",
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"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Gene Networks", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "SCOUP",
"Platform": "C++",
"Code": "https://github.com/hmatsu1226/SCOUP",
"Description": "Uses probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.",
"License": "MIT",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "SCUBA",
"Platform": "Matlab",
"DOI": "10.1073/pnas.1408993111",