anchor.diff:  demuxlet,C++,10.1101/118778;10.1038/nbt.4042,PREPRINT;2017-12-11,https://github.com/statgen/demuxlet,Genetic multiplexing of barcoded single cell RNA-seq,Apache-2.0,FALSE,FALSE,TRUE,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,FALSE,FALSE,FALSE,2017-03-29,2018-03-14
   DENDRO,R,10.1101/457622,PREPRINT,https://github.com/zhouzilu/DENDRO,"DENDRO, stands for Dna based EvolutioNary tree preDiction by scRna-seq technOlogy, is an R package, which takes scRNA-seq data for a tumor (or related somatic tissues) and accurately reconstructs its phylogeny, assigning each single cell from the single cell RNA sequencing (scRNA-seq) data to a subclone.",GPL-3,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,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,2018-11-14,2018-11-14
   DendroSplit,Python,10.1101/191254,PREPRINT,https://github.com/jessemzhang/dendrosplit,"DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets.",CC-BY-NC-SA,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,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,2018-01-19,2018-01-19
  -densityCut,R,10.1093/bioinformatics/btw227,2016-04-23,https://bitbucket.org/jerry00/densitycut_dev,densityCut is an efficient density-based clustering algorithm to analyze large-scale biological data,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,TRUE,FALSE,FALSE,2018-11-13,2018-11-13
  +densityCut,R,10.1093/bioinformatics/btw227,2016-04-23,https://bitbucket.org/jerry00/densitycut_dev,densityCut is an efficient density-based clustering algorithm to analyze large-scale biological data,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,TRUE,FALSE,FALSE,2018-11-13,2018-11-13
   DensityPath,R/Matlab,10.1101/276311;10.1093/bioinformatics/bty1009,PREPRINT;2018-12-07,https://github.com/ucasdp/DensityPath,DensityPath can accurately and efficiently reconstruct the underlying cell developmental trajectories for large-scale scRNAseq data.,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2018-03-08,2018-12-13
   DepecheR,R,10.1101/396135,PREPRINT,https://github.com/Theorell/DepecheR,DepecheR is an R package for clustering cytometry and single-cell RNA sequencing data according to a minimal set of biological markers,MIT,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2018-11-14,2018-11-14
   DESC,Python,10.1101/530378,PREPRINT,https://github.com/eleozzr/desc,DESC is an unsupervised deep learning algorithm for clustering scRNA-seq data.,,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2019-02-01,2019-02-01
  -DESCEND,R,10.1101/227033,PREPRINT,https://github.com/jingshuw/descend,DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts,GPL (>=2),FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-12-20,2017-12-20
  +DESCEND,R,10.1101/227033,PREPRINT,https://github.com/jingshuw/descend,DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts,GPL (>= 2),FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-12-20,2017-12-20
   DEsingle,R,10.1101/173997;10.1093/bioinformatics/bty332,PREPRINT;2018-04-24,https://github.com/miaozhun/DEsingle,DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It will detect differentially expressed genes between two groups of cells in a scRNA-seq raw read counts matrix.,GPL-2,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,2018-03-15,2018-05-01
   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,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-09,2017-04-18
   Dhaka,Python,10.1101/183863,PREPRINT,https://github.com/MicrosoftGenomics/Dhaka,Python module applying deep learning to improve clustering and other analysis of single-cell genomic data (gene expression and copy number variation).,MIT,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2018-11-13,2018-11-13
   DigitalCellSorter,Python,10.1101/539833,PREPRINT,https://github.com/sdomanskyi/DigitalCellSorter,Identification of hematological cell types from heterogeneous single cell RNA-seq data.,,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,2019-02-12,2019-02-12
  -DIMMSC,R,arxiv/1704.02007;10.1093/bioinformatics/btx490,PREPRINT;2017-09-02,https://www.pitt.edu/~wec47/singlecell.html,DIMMSC is an R package for clustering droplet-based single cell transcriptomic data. It uses Dirichlet mixture prior to characterize variations across different clusters.,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,TRUE,FALSE,FALSE,2018-03-15,2018-03-15
  +DIMMSC,R,arxiv/1704.02007;10.1093/bioinformatics/btx490,PREPRINT;2017-09-02,https://www.pitt.edu/~wec47/singlecell.html,DIMMSC is an R package for clustering droplet-based single cell transcriptomic data. It uses Dirichlet mixture prior to characterize variations across different clusters.,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,TRUE,FALSE,FALSE,2018-03-15,2018-03-15
   DISCO,Python,,,https://pbtech-vc.med.cornell.edu/git/mason-lab/disco/tree/master,Distributions of Isoforms in Single Cell Omics,,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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2018-03-15,2018-03-15
   DistMap,R,10.1126/science.aan3235,2017-10-31,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.,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,2017-09-01,2017-09-01
   DittoSeq,R,,,https://github.com/dtm2451/DittoSeq,A set of functions built to enable analysis and visualization of single cell and bulk RNA-sequencing data by novice and/or color blind coders,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,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2019-03-01,2019-03-01
  
positive.code: 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
  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
  
negative.code: scDAPA,Shell/R,10.1093/bioinformatics/btz701,2019-09-06,https://sourceforge.net/projects/scdapa/,scDAPA - a tool for detection and visualization of dynamic alternative polyadenylation from scRNA-seq dataq,GPL,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,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2019-09-17,2019-09-17
  SCDC,R,10.1101/743591,PREPRINT,https://github.com/meichendong/SCDC,SCDC: Deconvolution of Bulk Gene Expression by Single-Cell RNA Sequencing 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,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2019-09-26,2019-09-26
  scDD,R,10.1101/035501;10.1186/s13059-016-1077-y,PREPRINT;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-2,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,TRUE,2016-09-08,2018-03-14
  scDDboost,R,10.1101/655795,PREPRINT,https://github.com/wiscstatman/scDDboost,Identifying differential distributed genes for single cell RNA data,GPL (>= 2),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,2019-06-06,2019-06-06
  SCDE,R,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.,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2016-09-08,2016-09-08
  scDeepCluster,Python,10.1038/s42256-019-0037-0,2019-04-09,https://github.com/ttgump/scDeepCluster,"scDeepCluster, a model-based deep embedded clustering for Single Cell RNA-seq data.",Apache-2.0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2019-04-16,2019-04-16
  scDesign,R,10.1101/437095,PREPRINT,https://github.com/Vivianstats/scDesign,scDesign: a statistical simulator for rational scRNA-seq experimental design,GPL-2,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,FALSE,FALSE,TRUE,2019-04-05,2019-04-05
  