Last version

Release 2: 2017-07-09
New version including:
- changing reading and writing methods to the 'data.table' package to speed up processing of big datasets
- handling of 69 organisms (from all releases of Ensembl database)
- addition of alternative gene names from old Ensembl releases
- correction of minor bugs
- update of R and Python libraries
Tool/DB Tool type Reference Version
JSC [Java statistical API] Java http://www.jsc.nildram.co.uk/api/index.html 1.0
scLVM R Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC & Stegle O, 2015. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-Sequencing data reveals hidden subpopulation of cells, Nature Biotechnology, doi: 10.1038/nbt.3102. 0.99.3
GPy Python https://github.com/SheffieldML/GPy 1.5.6
limix Python https://github.com/PMBio/limix 0.8.0.dev0
h5py Python http://docs.h5py.org/en/latest/build.html 2.6.0
scikits.learn Python http://scikit-learn.org/stable/ 0.18.1
numpy Python http://www.numpy.org/index.html 1.11.2
scipy Python https://www.scipy.org/ 0.18.1
Java JDK Java http://www.oracle.com/technetwork/java/javase/downloads 1.8.0_111
Python Python https://www.python.org/ 2.7.5
ComBat [sva package] R Johnson, WE, Rabinovic, A, and Li, C (2007). Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics 8(1):118-127. 3.24.4
R version & default packages [stats] (K-means, PCA, Hierarchical clustering, distance, correlation) R https://www.r-project.org/ 3.3.1
SC3 R Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR and Hemberg M (2016). “SC3 - consensus clustering of single-cell RNA-Seq data.” bioRxiv. doi: 10.1101/036558, http://biorxiv.org/content/early/2016/09/02/036558. 1.3.11
MDS [MASS Package] R Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0 7.3-45
PAM / Silhouette Plot [cluster package] R Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.(2016). cluster: Cluster Analysis Basics and Extensions. 2.0.6
ZIFA Python Emma Pierson and Christopher YauEmail, ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis, Genome Biology, 16:241, 2015, DOI: 10.1186/s13059-015-0805-z 0.1
Rtsne R L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. 0.13
Pagoda / SCDE R Peter V Kharchenko, Lev Silberstein and David T Scadden, Bayesian approach to single-cell differential expression analysis, Nature Methods 11, 740–742 (2014) doi:10.1038/nmeth.2967 2.5.0
Limma / Voom R Charity W Law, Yunshun Chen, Wei Shi and Gordon K Smyth, voom: precision weights unlock linear model analysis tools for RNA-seq read counts, Genome Biology, 15:R29, 2014 DOI: 10.1186/gb-2014-15-2-r29 3.32.2
edgeR R Mark D. Robinson, Davis J. McCarthy, and Gordon K. Smyth, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics. 2010 Jan 1; 26(1): 139–140. doi: 10.1093/bioinformatics/btp616. PMCID: PMC2796818 3.18.1
DESeq2 R Love MI, Huber W and Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, pp. 550. doi: 10.1186/s13059-014-0550-8. 1.16.1
SCAN / UPC R Piccolo SR, Withers MR, Francis OE, Bild AH and Johnson WE (2013). “Multi-platform single-sample estimates of transcriptional activation.” Proceedings of the National Academy of Sciences of the United States of America, 110(44), pp. 17778-17783. doi: 10.1016/j.ygeno.2012.08.003. 2.18.0
data.table R https://github.com/Rdatatable/data.table/wiki 1.10.4
Gene Ontology Database http://geneontology.org/ 2017-Jun
KEGG Database http://www.genome.jp/kegg/ 2016-Nov
MSigDB from GSEA Database http://software.broadinstitute.org/gsea/downloads.jsp 2016-Nov
Gene Atlas Database https://www.ebi.ac.uk/gxa/home 2016-Nov
Ensembl Database Ensembl Database (GTF files) 2017-Mar