anchor.diff:  SINCERITIES,MATLAB/R,10.1093/bioinformatics/btx575,2017-09-14,https://github.com/CABSEL/SINCERITIES,SINCERITIES is a tool for inferring gene regulatory networks from time-stamped cross-sectional single cell transcriptional expression profiles.,BSD-3-clause,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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-09-21,2017-09-21
   SinCHet,MATLAB,10.1093/bioinformatics/btx297,2017-05-04,http://labpages2.moffitt.org/chen/software,a MATLAB GUI toolbox for Single Cell Heterogeneity analysis in cancer,,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,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2017-11-20,2017-11-20
   SINGE,MATLAB,10.1101/534834,PREPRINT,https://github.com/gitter-lab/SINGE,SINGE (Single-cell Inference of Networks using Granger Ensembles) reconstructs gene regulatory networks from pseudotime-annotated scRNA-seq data,MIT,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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2019-02-05,2019-05-17
  -SingleCellExplorer,Python,10.1186/s12864-019-6053-y,2019-08-27,https://github.com/d-feng/scExplorer,Single Cell Explorer is a Python-based web server application for computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform.,LGPL-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,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2019-09-02,2019-09-02
  +SingleCellExplorer,Python,10.1186/s12864-019-6053-y,2019-08-27,https://github.com/d-feng/scExplorer,Single Cell Explorer is a Python-based web server application for computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform.,LGPL-3.0,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,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2019-09-02,2019-09-02
   singleCellHaystack,R,10.1101/557967,PREPRINT,https://github.com/alexisvdb/singleCellHaystack,singleCellHaystack is a package for finding surprising needles (=genes) in haystacks (=multi-dimensional representations of single cell transcriptome data),MIT,FALSE,FALSE,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,TRUE,FALSE,FALSE,2019-02-25,2019-05-07
   SingleCellNet,R,10.1016/j.cels.2019.06.004,2019-07-31,https://github.com/pcahan1/singleCellNet,SingleCellNet enables the classifcation of single cell RNA-Seq data across species and platforms,MIT,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,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2019-08-12,2019-08-12
   SingleCellSignatureExplorer,GO/R,10.1101/621805,PREPRINT,https://sites.google.com/site/fredsoftwares/products/single-cell-signature-explorer,Single-Cell Signature Explorer is a package of four software tools dedicated to high throughput signature exploration in single-cell RNA-seq analysis. ,,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,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2019-05-07,2019-05-07
  
positive.code: pcaReduce,R,10.1186/s12859-016-0984-y,2016-03-22,https://github.com/JustinaZ/pcaReduce,Hierarchical clustering of single cell transcriptional profiles,GPL (>= 2),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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-09,2016-09-09
  pCMF,R,10.1101/211938,PREPRINT,https://gitlab.inria.fr/gdurif/pCMF,"A probabilistic Count Matrix Factorization (pCMF) approach for single-cell expression data analysis, that relies on a sparse Gamma-Poisson factor model.",GPL (>= 2),FALSE,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,FALSE,TRUE,FALSE,FALSE,2017-11-08,2017-11-08
  pCreode,Python,10.1016/j.cels.2017.10.012,2017-11-15,https://github.com/KenLauLab/pCreode,"The term creode was coined by C.H. Waddington, combining the Greek words for “necessary” and “path” to describe the cell state transitional trajectories that define cell fate specification. Our algorithm aims to identify consensus routes from relatively noisy single-cell data and thus we named this algorithm p- (putative) Creode.",Apache-2.0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-12-07,2017-12-07
  peco,R,10.1101/526848,PREPRINT,https://github.com/jhsiao999/peco,peco is an R package for predicting cell cycle progression in a continnum using scRNA-seq data.,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,FALSE,FALSE,FALSE,FALSE,FALSE,2019-02-11,2019-02-11
  PHATE,Python/MATLAB/R,10.1101/120378,PREPRINT,https://github.com/KrishnaswamyLab/PHATE,PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories.,GPL-2,FALSE,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,FALSE,TRUE,FALSE,FALSE,2017-11-20,2018-05-25
  PhenoPath,R,10.1101/159913;10.1038/s41467-018-04696-6,PREPRINT;2018-06-22,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. ,Apache-2.0,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,FALSE,FALSE,FALSE,FALSE,TRUE,2017-07-16,2018-06-27
  PIVOT,R,10.1101/053348;10.1186/s12859-017-1994-0,PREPRINT;2018-01-05,https://github.com/qinzhu/PIVOT,PIVOT facilitates fast and interactive RNA-Seq data analysis and visualization.,Custom,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2017-07-16,2018-03-14
  
negative.code: RCA,R,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).",MIT,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,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-03-27,2017-03-27
  rCASC,R/Docker,10.1101/430967,PREPRINT,https://github.com/kendomaniac/rCASC,"rCASC, Cluster Analysis of Single Cells",GPL-3,FALSE,TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2018-11-14,2018-11-14
  reCAT,R,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.,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-07-16,2017-07-16
  RESCUE,R,10.1186/s12859-019-2977-0,2019-07-12,https://github.com/seasamgo/rescue,Bootstrap imputation for scRNAseq data,GPL (>= 2),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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2019-07-24,2019-07-24
  robustSingleCell,R,10.1101/543199,PREPRINT,https://github.com/asmagen/robustSingleCell,robustSingleCell is a pipeline designed to identify robust cell subpopulations using scRNAseq data and compare population compositions across tissues and experimental models via similarity analysis,Artistic-2.0,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2019-03-15,2019-03-15
  ROSeq,MATLAB,10.1101/374025,PREPRINT,https://github.com/malaalam/ROSeq,ROSeq evaluates differential expression between two subpopulations of single cells by modeling the rank distribution of read counts for each gene,,FALSE,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,FALSE,FALSE,FALSE,FALSE,2018-07-27,2018-07-27
  rpca,R,10.1089/cmb.2018.0255,2019-05-01,https://github.com/macieksk/rpca,"RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components, Truncated RobustPCA with L2 noise. ",GPL (>= 2),FALSE,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,FALSE,FALSE,FALSE,FALSE,2019-05-08,2019-05-08
  RSCORE,R,10.1101/699959,PREPRINT,https://github.com/wycwycpku/RSCORE,RSCORE is an R package to explore single-cell RNA-seq data with the view of molecular networks,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2019-08-12,2019-08-12
  SAFE,R,10.1101/215723;10.1093/bioinformatics/bty793,PREPRINT;2018-09-08,https://github.com/yycunc/SAFEclustering,SAFE (Single-cell Aggregated clustering From Ensemble): Cluster ensemble for single-cell RNA-seq data,,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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-11-21,2018-10-02
  SAIC,R,10.1186/s12864-017-4019-5,2017-10-03,https://github.com/xiweiwu/SAIC,Single cell RNA-seq analysis with iterative clustering (SAIC) is a method to identify subgroups and signature genes in single cell RNA-seq data.,LGPL (>= 2.0),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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-10-10,2017-10-10
  sake,R,,,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),FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2016-10-10,2016-10-10
  salmon,C++,10.1101/021592;10.1038/nmeth.4197;10.1101/335000;10.1186/s13059-019-1670-y,PREPRINT;2017-03-06;PREPRINT;2019-03-27,https://github.com/COMBINE-lab/salmon,Salmon produces transcript-level quantification estimates for RNA-seq data. It includes the Alevin pipeline for quantifying droplet scRNA-seq.,GPL-3,FALSE,FALSE,TRUE,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,FALSE,FALSE,FALSE,2018-06-08,2019-04-05
  SAMstrt,R,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-3.0,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,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
  