pcaReduce 1.0 – Hierarchical Clustering of Single Cell Transcriptional Profiles

pcaReduce 1.0

:: DESCRIPTION

pcaReduce is a novel agglomerative clustering method to generate a cell state hierarchy where each cluster branch is associated with a principal component of variation that can be used to differentiate two cell states.

::DEVELOPER

pcaReduce team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

 pcaReduce

:: MORE INFORMATION

Citation

pcaReduce: hierarchical clustering of single cell transcriptional profiles.
Žurauskienė J, Yau C.
BMC Bioinformatics. 2016 Mar 22;17(1):140. doi: 10.1186/s12859-016-0984-y.

pvclust 2.0-0 – Hierarchical Clustering with P-values

pvclust 2.0-0

:: DESCRIPTION

pvclust is an R package for assessing the uncertainty in hierarchical cluster analysis. For each cluster in hierarchical clustering, quantities called p-values are calculated via multiscale bootstrap resampling. P-value of a cluster is a value between 0 and 1, which indicates how strong the cluster is supported by data.

::DEVELOPER

Shimodaira Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R package

:: DOWNLOAD

 pvclust

:: MORE INFORMATION

Citation

Pvclust: an R package for assessing the uncertainty in hierarchical clustering.
Suzuki R, Shimodaira H.
Bioinformatics. 2006 Jun 15;22(12):1540-2. Epub 2006 Apr 4.

HHCompare – HMM based protein Hierarchical Clustering

HHCompare

:: DESCRIPTION

HHCompare is a pipeline for HMM-HMM comparison based hierarchial clustering and analysis of potential paralogues in sequence set.

::DEVELOPER

Ranko Gacesa, King’s College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 HHCompare

:: MORE INFORMATION

Citation

Gene duplications are extensive and contribute significantly to the toxic proteome of nematocysts isolated from Acropora digitifera (Cnidaria: Anthozoa: Scleractinia).
Gacesa R, Chung R, Dunn SR, Weston AJ, Jaimes-Becerra A, Marques AC, Morandini AC, Hranueli D, Starcevic A, Ward M, Long PF.
BMC Genomics. 2015 Oct 13;16(1):774. doi: 10.1186/s12864-015-1976-4.

CLAG 2.18.1 – Unsupervised Non Hierarchical Clustering algorithm

CLAG 2.18.1

:: DESCRIPTION

CLAG (for CLusters AGgregation) is an unsupervised non hierarchical clustering algorithm designed to cluster a large variety of biological data and to provide a clustered matrix and numerical values indicating cluster strength.

::DEVELOPER

Laboratory of Computational and Quantitative Biology(LCQB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

 CLAG

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2012 Aug 8;13:194. doi: 10.1186/1471-2105-13-194.
CLAG: an unsupervised non hierarchical clustering algorithm handling biological data.
Dib L1, Carbone A.

GDHC – Nonlinear Hierarchical Clustering

GDHC

:: DESCRIPTION

GDHC (General Dependency Hierarchical Clustering) is a method of hierarchical clustering for high throughput data. Nonlinear relations exist between features of high-throughput data. They reflect critical regulation patterns in the biological system. Clustering based on both linear and nonlinear relations in high throughput data is hampered by the high dimensionality and high noise level in the data.

::DEVELOPER

Tianwei Yu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R

:: DOWNLOAD

 GDHC

:: MORE INFORMATION

Citation

IEEE/ACM Trans Comput Biol Bioinform. 2013 Jul-Aug;10(4):1080-5. doi: 10.1109/TCBB.2013.99.
Hierarchical clustering of high-throughput expression data based on general dependences.
Yu T, Peng H

VisHiC – Visualisation of Hierarchical Clustering based on Functional Annotations

VisHiC

:: DESCRIPTION

VisHiC is a public web server for clustering and interpreting gene expression data. The tool is designed to extract the most significant biological features of a dataset in a single run.

::DEVELOPER

Bioinformatics, Algorithmics and Data Mining Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W587-92. doi: 10.1093/nar/gkp435. Epub 2009 May 29.
VisHiC–hierarchical functional enrichment analysis of microarray data.
Krushevskaya D1, Peterson H, Reimand J, Kull M, Vilo J.

ESPRIT-Tree 1.2 – Hierarchical Clustering Analysis of Massive Sequence data

ESPRIT-Tree 1.2

:: DESCRIPTION

ESPRIT-Tree is a software for hierarchical clustering analysis of massive sequence data.

::DEVELOPER

Bioinformatics Laboratory, SUNY Buffalo

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  ESPRIT-Tree

:: MORE INFORMATION

Citation:

Y. Cai and Y. Sun
ESPRIT-Tree: Hierarchical Clustering Analysis of Millions of 16S rRNA Pyrosequences in Quasilinear Time,
Nucleic Acids Research, vol. 39, no. 14, e95, 2011. (impact factor: 7.8)

DomClust – Hierarchical Clustering for Orthologous Domain Classification

DomClust

:: DESCRIPTION

DomClust is an effective tool for orthologous grouping in multiple genomes, which is a crucial first step in large-scale comparative genomics.

::DEVELOPER

Ikuo Uchiyama (uchiyama@nibb.ac.jp)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux
  • C Compiler

:: DOWNLOAD

 DomClust

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2006 Jan 25;34(2):647-58.
Hierarchical clustering algorithm for comprehensive orthologous-domain classification in multiple genomes.
Uchiyama I.

dbc454 1.43 – Density-based Hierarchical Clustering of Pyro-sequences

dbc454 1.43

:: DESCRIPTION

DBC454 is a density-based hierarchical clustering algorithm.

::DEVELOPER

Vital-IT Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX

:: DOWNLOAD

 DBC454

:: MORE INFORMATION

Citation

Bioinformatics. 2013 May 15;29(10):1268-74. doi: 10.1093/bioinformatics/btt149. Epub 2013 Mar 28.
Density-based hierarchical clustering of pyro-sequences on a large scale–the case of fungal ITS1.
Pagni M, Niculita-Hirzel H, Pellissier L, Dubuis A, Xenarios I, Guisan A, Sanders IR, Goudet J, Guex N.

BHC 1.1.0 – Bayesian Hierarchical Clustering for R

BHC 1.1.0

:: DESCRIPTION

BHC is an R/Bioconductor port of the fast novel algorithm for Bayesian agglomerative hierarchical clustering.

::DEVELOPER

Warwick Systems Biology Centre

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows/Linux/ MacOsX
  • R package

:: DOWNLOAD

 BHC

:: MORE INFORMATION

Citation

BMC Bioinformatics 2009, 10:242
R/BHC: fast Bayesian hierarchical clustering for microarray data
Richard S Savage et al.