anchor.diff:  Scater,R,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),FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2016-09-08,2017-03-01
   scDD,R,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-2,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,2016-09-08,2016-11-07
   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,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
  -SCell,MATLAB,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.",GNU,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2016-09-08,2016-09-08
  +SCell,MATLAB,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.",GPL,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,2016-09-08,2016-09-08
   SCENIC,R,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.,Custom,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,2017-06-26,2017-06-26
   SCENT,R,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-3,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,FALSE,2016-11-09,2017-06-08
   SCIMITAR,Python,10.1101/070151,PREPRINT,https://github.com/dimenwarper/scimitar,SCIMITAR provides a variety of tools to analyze trajectory maps of single-cell measurements.,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-12,2016-09-12
  
positive.code: ZIFA,Python,10.1186/s13059-015-0805-z,2015-11-02,https://github.com/epierson9/ZIFA,Zero-inflated dimensionality reduction algorithm for single-cell data.,MIT,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,2016-12-22,2016-12-22
  ZINB-WaVE,R,10.1101/125112,PREPRINT,https://github.com/drisso/zinbwave,Zero-inflated Negative Binomial based Wanted Variation Extraction (ZINB-WaVE),,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,2017-04-27,2017-07-03
  zingeR,R,10.1101/157982,PREPRINT,https://github.com/statOmics/zingeR,zingeR: Zero-Inflated Negative binomial Gene Expression in R.,,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,2017-07-03,2017-07-03
  zUMIs,Shell,10.1101/153940,PREPRINT,https://github.com/sdparekh/zUMIs,zUMIs is a fast and flexible pipeline to process RNA-seq data with UMIs,,FALSE,TRUE,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,TRUE,FALSE,FALSE,2017-06-30,2017-09-11
negative.code: 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,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,TRUE,FALSE,FALSE,2017-01-16,2017-01-16
  dropClust,R/C++/Python,10.1101/170308,PREPRINT,https://github.com/debsin/dropClust,Efficient clustering of ultra-large scRNA-seq data,GPL (>= 3),FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-08-20,2017-08-20
  dropEst,C++/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-3,FALSE,FALSE,TRUE,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,TRUE,FALSE,FALSE,2017-08-20,2017-09-11
  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,FALSE,TRUE,TRUE,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,TRUE,FALSE,FALSE,2017-05-21,2017-09-11
  DrSeq2,Python,10.1101/143271,PREPRINT,https://github.com/ChengchenZhao/DrSeq2,Quality control and analysis pipeline for parallel single cell transcriptome and epigenome data,GPL-3,FALSE,TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-06-26,2017-09-11
  DTWScore,R,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,,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-05-30,2017-05-30
  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,FALSE,FALSE,TRUE,FALSE,TRUE,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
  