anchor.diff:  scVEGs,R,10.1186/s12864-016-2897-6,2016-08-22,https://github.com/hillas/scVEGs,scVEGs is a novel algorithm for single-cell RNA-seq (scRNA-seq) data to determine significant variably expressed genes (VEGs) using a gene expression variation model (GEVM).,MIT,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-09,2016-09-09
   SEPA,R,,,https://github.com/zji90/SEPA,"SEPA provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns.",GPL (>=2),FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,2016-09-08,2016-09-08
   Seurat,R,10.1038/nbt.3192,2015-04-13,https://github.com/satijalab/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.",GPL-3,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-08,2016-10-07
  -SIMLR,R,10.1038/nmeth.4207,2017-03-06,https://github.com/BatzoglouLabSU/SIMLR,"Single-cell Interpretation via Multi-kernel LeaRning which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. ",GPL-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-09,2017-03-10
  +SIMLR,R,10.1038/nmeth.4207,2017-03-06,https://github.com/BatzoglouLabSU/SIMLR,"Single-cell Interpretation via Multi-kernel LeaRning which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.",GPL-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-09,2017-03-10
   sincell,R,10.1101/014472,PREPRINT,https://github.com/Bioconductor-mirror/sincell,"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.",GPL (>= 2),FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-08,2016-09-08
  -Sincera,R,10.1371/journal.pcbi.1004575,2015-11-04,https://research.cchmc.org/pbge/sincera.html,R-based pipeline for single-cell analysis including clustering and visualization.,GPL-3,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-08,2016-09-08
  +Sincera,R,10.1371/journal.pcbi.1004575,2015-11-04,https://research.cchmc.org/pbge/sincera.html,R-based pipeline for single-cell analysis including clustering and visualization.,GPL-3,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-08,2017-04-18
   SingleSplice,R/Perl/C++,10.1093/nar/gkv1525,2016-01-05,https://github.com/jw156605/SingleSplice,Algorithm for detecting alternative splicing in a population of single cells.,GPL-2,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-01-24,2017-01-24
  -SinQC,R/Python,10.1093/bioinformatics/btw176,2016-04-10,http://www.morgridge.net/SinQC.html,A Method and Tool to Control Single-cell RNA-seq Data Quality.,,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-08,2016-09-08
  +SinQC,R/Python,10.1093/bioinformatics/btw176,2016-04-10,http://www.morgridge.net/SinQC.html,A Method and Tool to Control Single-cell RNA-seq Data Quality.,,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-08,2017-04-18
   SLICE,R,10.1093/nar/gkw1278,2016-12-19,https://research.cchmc.org/pbge/slice.html,The SLICE (Single Cell Lineage Inference Using Cell Expression Similarity and Entropy) algorithm consists of two major steps: (1) measuring cell differentiation states based on the calculation of single cell entropy (scEntropy) and (2) predicting cell differentiation trajectories by ordering single cells according to their scEntropy-derived differentiation states.,GPL-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-01-10,2017-01-10
   SLICER,R,10.1186/s13059-016-0975-3,2016-05-23,https://github.com/jw156605/SLICER,Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories.,GPL-2,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-08,2016-09-08
   slingshot,R,,,https://github.com/kstreet13/slingshot,Functions for identifying and characterizing continuous developmental trajectories in single-cell sequencing data.,Artistic-2.0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-09,2016-09-09
  -SPADE,R,10.1038/nprot.2016.066,2016-06-16,https://github.com/nolanlab/spade,Visualization and cellular hierarchy inference of single-cell data using SPADE.,GPL (>=2),FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-09,2016-09-09
  +SPADE,R,10.1038/nprot.2016.066,2016-06-16,https://github.com/nolanlab/spade,Visualization and cellular hierarchy inference of single-cell data using SPADE.,GPL (>=2),FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-09,2017-04-18
   Splatter,R,,,https://github.com/Oshlack/splatter,Splatter is a package for the simple simulation of single-cell RNA-seq count data.,GPL-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,2016-10-27,2016-10-27
   SPRING,Python/Matlab/Javascript,10.1101/090332,PREPRINT,https://github.com/AllonKleinLab/SPRING/,SPRING is a collection of pre-processing scripts and a web browser-based tool for visualizing and interacting with high dimensional data.,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2016-12-05,2016-12-05
   SSrGE,Python,10.1101/095810,PREPRINT,https://github.com/lanagarmire/SSrGE,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.,MIT,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-01-10,2017-01-10
  
positive.code: GiniClust,R/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.,MIT,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,2016-09-09,2016-09-09
  GPfates,Python,10.1126/sciimmunol.aal2192,2017-03-03,https://github.com/Teichlab/GPfates,Model transcriptional cell fates as mixtures of Gaussian Processes,MIT,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-19,2017-03-10
  Granatum,Virtualbox,10.1101/110759,PREPRINT,https://gitlab.com/uhcclxgg/granatum,This is a graphical single-cell RNA-seq (scRNA-seq) analysis pipeline for genomics scientists.,,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2017-03-01,2017-03-01
  GRM,R,10.1093/bioinformatics/btv122,2015-07-01,http://wanglab.ucsd.edu/star/GRM/,Normalization and noise reduction for singlecell RNA-seq experiments,,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-09,2016-09-09
  HocusPocus,R,,,https://github.com/joeburns06/hocuspocus,Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data.,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-08,2016-09-08
  ISOP,R,10.1101/036988,PREPRINT,https://github.com/nghiavtr/ISOP,ISOform-level expression Patterns in single-cell RNA-sequencing data.,GPL-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-09,2016-09-09
  k-branches,R,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-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-12-22,2016-12-22
  LEAP,R,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-2,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-12,2017-01-10
  Linnorm,R,,,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.",MIT,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,2017-02-01,2017-02-01
  M3Drop,R,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),FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-10-10,2016-10-10
  MAGIC,Python,10.1101/111591,PREPRINT,https://github.com/pkathail/magic/,Markov Affinity-based Graph Imputation of Cells (MAGIC),GPL-2,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2017-02-27,2017-02-17
  
negative.code: D3E,Python,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-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-09,2016-09-09
  DeLorean,R,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.,MIT,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-09,2016-09-09
  demuxlet,C++,10.1101/118778,PREPRINT,https://github.com/hyunminkang/apigenome,Sample demultiplexing of droplet sc-RNAseq reads.,GPL-3,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-03-29,2017-03-29
  destiny,R,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.,GPL,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-09,2017-04-18
  DPT,R/Matlab,10.1038/nmeth.3971,2019-08-29,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-3,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-22,2016-09-22
  dropbead,R,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-3,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-01-16,2017-01-16
  ECLAIR,Python,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.",MIT,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-08,2016-09-08
  