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scClustViz - Single-cell RNAseq cluster assessment and visualization.

Innes BT, Bader GD.

Version 2. F1000Res. 2018 Sep 21 [revised 2019 Jan 1];7. pii: ISCB Comm J-1522. doi: 10.12688/f1000research.16198.2. eCollection 2018.


funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data.

Kolberg L, Kuzmin I, Adler P, Vilo J, Peterson H.

BMC Genomics. 2018 Nov 14;19(1):817. doi: 10.1186/s12864-018-5176-x.


ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.

Gardeux V, David FPA, Shajkofci A, Schwalie PC, Deplancke B.

Bioinformatics. 2017 Oct 1;33(19):3123-3125. doi: 10.1093/bioinformatics/btx337.


Clustering trees: a visualization for evaluating clusterings at multiple resolutions.

Zappia L, Oshlack A.

Gigascience. 2018 Jul 1;7(7). doi: 10.1093/gigascience/giy083.


Metric for measuring the effectiveness of clustering of DNA microarray expression.

Loganantharaj R, Cheepala S, Clifford J.

BMC Bioinformatics. 2006 Sep 6;7 Suppl 2:S5.


Knowledge-assisted recognition of cluster boundaries in gene expression data.

Okada Y, Sahara T, Mitsubayashi H, Ohgiya S, Nagashima T.

Artif Intell Med. 2005 Sep-Oct;35(1-2):171-83.


DEBrowser: interactive differential expression analysis and visualization tool for count data.

Kucukural A, Yukselen O, Ozata DM, Moore MJ, Garber M.

BMC Genomics. 2019 Jan 5;20(1):6. doi: 10.1186/s12864-018-5362-x.


AgriSeqDB: an online RNA-Seq database for functional studies of agriculturally relevant plant species.

Robinson AJ, Tamiru M, Salby R, Bolitho C, Williams A, Huggard S, Fisch E, Unsworth K, Whelan J, Lewsey MG.

BMC Plant Biol. 2018 Sep 19;18(1):200. doi: 10.1186/s12870-018-1406-2.


FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.

Scala G, Serra A, Marwah VS, Saarimäki LA, Greco D.

BMC Bioinformatics. 2019 Feb 15;20(1):79. doi: 10.1186/s12859-019-2639-2.


clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.

Risso D, Purvis L, Fletcher RB, Das D, Ngai J, Dudoit S, Purdom E.

PLoS Comput Biol. 2018 Sep 4;14(9):e1006378. doi: 10.1371/journal.pcbi.1006378. eCollection 2018 Sep. Erratum in: PLoS Comput Biol. 2019 Jan 11;15(1):e1006727.


A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

Sun S, Chen Y, Liu Y, Shang X.

BMC Syst Biol. 2019 Apr 5;13(Suppl 2):28. doi: 10.1186/s12918-019-0699-6.


Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data.

Wang T, Li B, Nelson CE, Nabavi S.

BMC Bioinformatics. 2019 Jan 18;20(1):40. doi: 10.1186/s12859-019-2599-6.


TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.

Ji Z, Ji H.

Nucleic Acids Res. 2016 Jul 27;44(13):e117. doi: 10.1093/nar/gkw430. Epub 2016 May 13.


Identifying cell populations with scRNASeq.

Andrews TS, Hemberg M.

Mol Aspects Med. 2018 Feb;59:114-122. doi: 10.1016/j.mam.2017.07.002. Epub 2017 Jul 25. Review.


iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform.

Choi K, Ratner N.

BMC Genomics. 2019 Mar 6;20(1):177. doi: 10.1186/s12864-019-5548-x.


C-DEVA: Detection, evaluation, visualization and annotation of clusters from biological networks.

Li M, Tang Y, Wu X, Wang J, Wu FX, Pan Y.

Biosystems. 2016 Dec;150:78-86. doi: 10.1016/j.biosystems.2016.08.004. Epub 2016 Aug 13.


A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.

Zhang H, Lee CAA, Li Z, Garbe JR, Eide CR, Petegrosso R, Kuang R, Tolar J.

PLoS Comput Biol. 2018 Apr 9;14(4):e1006053. doi: 10.1371/journal.pcbi.1006053. eCollection 2018 Apr.


Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking.

McMurdie PJ, Holmes S.

Bioinformatics. 2015 Jan 15;31(2):282-3. doi: 10.1093/bioinformatics/btu616. Epub 2014 Sep 26.


DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

Sun Z, Wang T, Deng K, Wang XF, Lafyatis R, Ding Y, Hu M, Chen W.

Bioinformatics. 2018 Jan 1;34(1):139-146. doi: 10.1093/bioinformatics/btx490.

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