anchor.diff:  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,TRUE,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,TRUE,FALSE,FALSE,FALSE,TRUE,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,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,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,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,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,TRUE,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,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2016-09-12,2016-09-12
   scImpute,R,10.1101/141598,PREPRINT,https://github.com/Vivianstats/scImpute,scImpute is developed to accurately and robustly impute the dropout values in scRNA-seq data,,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,2017-05-30,2017-05-30
  
positive.code: 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,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,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,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,FALSE,TRUE,FALSE,FALSE,2017-01-16,2017-01-16
  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.,,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,TRUE,FALSE,FALSE,2017-05-21,2017-05-21
  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,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,2017-05-30,2017-05-30
  
negative.code: Name,Platform,DOI,PubDate,Code,Description,License,Assembly,Quantification,QualityControl,Normalisation,Imputation,GeneFiltering,Clustering,Classification,Pseudotime,DifferentialExpression,MarkerGenes,ExpressionPatterns,VariableGenes,GeneSets,GeneNetworks,CellCycle,DimensionalityReduction,Transformation,Modality,AlternativeSplicing,RareCells,StemCells,Variants,Haplotypes,AlleleSpecific,Visualisation,Interactive,Simulation,Added,Updated
  anchor,Python,,,https://github.com/yeolab/anchor,"Find bimodal, unimodal, and multimodal features in your data",BSD-3-clause,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,2016-09-08,2016-09-08
  AUCell,R,10.1101/144502,PREPRINT,https://github.com/aertslab/AUCell,AUCell is an R-package to analyze the state of gene-sets in single-cell RNA-seq data (i.e. identify cells with active gene signatures).,Custom,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,TRUE,FALSE,FALSE,2017-06-26,2017-06-26
  BackSPIN,Python,10.1126/science.aaa1934,2015-03-06,https://github.com/linnarsson-lab/BackSPIN,Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.,BSD 2-clause,FALSE,FALSE,FALSE,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,2016-09-08,2016-09-08
  badger,R,,,https://github.com/JEFworks/badger,Bayesian approach for detecting copy number alterations from 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,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,2017-06-05,2017-06-05
  BASIC,Python,10.1093/bioinformatics/btw631,2016-09-28,http://ttic.uchicago.edu/~aakhan/BASIC/,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.,MIT,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,2016-10-10,2016-10-10
  