SCUDO – Signature-based Clustering of Expression Profiles

SCUDO

:: DESCRIPTION

SCUDO (Signature-based ClUstering for DiagnOstic purposes) is a tool for clustering gene expression profiles for diagnostic purposes using a new type of rank-based signatures

::DEVELOPER

The Microsoft Research – University of Trento Centre for Computational and Systems Biology

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

SCUDO: a tool for signature-based clustering of expression profiles.
Lauria M, Moyseos P, Priami C.
Nucleic Acids Res. 2015 May 9. pii: gkv449.

Pathway – Heritable Clustering Algorithms

Pathway

:: DESCRIPTION

Pathway uses methylation profiles and clinical variables to group tumor samples into clusters and then organize them into a tree to represent tumor progression pathways that conform to strict heritability.

::DEVELOPER

Statistical Genetics and Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MATLAB

:: DOWNLOAD

  Pathway

:: MORE INFORMATION

Citation

Wang, Z., Yan, P., Potter, D., Eng, C., Huang, T.H., Lin, S. (2005)
Heritable Clustering Algorithms – Recapitulation of Breast Tumor Progression Pathways Using DNA Methylation Data.

Sparcl – Arbitrary Shape Clustering

Sparcl

:: DESCRIPTION

Sparcl finds shape-based clusters. It uses a two step approach: in the first step we select a relatively large number of candidate centroids (via ROBIN) to find seed clusters via the K-means algorithm and in the second step we use a novel similarity kernel to merge the initial seed clusters to yield the final arbitrary shaped clusters.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

Sparcl

:: MORE INFORMATION

Citation

Vineet Chaoji, Mohammad Al Hasan, Saeed Salem and Mohammed J. Zaki (2009)
SPARCL: An Effective and Efficient Algorithm for Mining Arbitrary Shape-based Clusters.
Knowledge and Information Systems, 21(2), Nov, pp.201-229.

TriCluster / MicroCluster – Microarray Gene Expression Clustering

TriCluster / MicroCluster

:: DESCRIPTION

Tricluster is the first tri-clustering algorithm for microarray expression clustering. It builds upon the new microCluster bi-clustering approach. Tricluster first mines all the bi-clusters across the gene-sample slices, and then it extends these into tri-clusters across time or space (depending on the third dimension). It can find both scaling and shifting patterns

MicroCluster is a deterministic biclustering algorithm that can mine arbitrarily positioned and overlapping clusters of gene expression data to find interesting patterns

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 TriCluster / MicroCluster

:: MORE INFORMATION

Citation

Lizhuang Zhao and Mohammed J. Zaki,
TriCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data.
In ACM SIGMOD Conference on Management of Data. Jun 2005.

Lizhuang Zhao and Mohammed J. Zaki,
MicroCluster: An Efficient Deterministic Biclustering Algorithm for Microarray Data.
IEEE Intelligent Systems, 20(6):40-49. Nov/Dec 2005

GiniClust3 1.0.1 – Detecting Rare Cell Types from Single-cell Gene Expression data with Gini Index

GiniClust 3 1.0.1

:: DESCRIPTION

GiniClust is a clustering method specifically designed for rare cell type detection. It uses the Gini index to identify genes that are associated with rare cell types without prior knowledge.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

GiniClust

:: MORE INFORMATION

Citation

Rui Dong. Guo-Cheng Yuan.
GiniClust3: a fast and memory-efficient tool for rare cell type identification.

Genome Biol, 17 (1), 144 2016 Jul 1
GiniClust: Detecting Rare Cell Types From Single-Cell Gene Expression Data With Gini Index
Lan Jiang, Huidong Chen, Luca Pinello, Guo-Cheng Yuan

Tsoucas D, Yuan GC.
GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection.
Genome Biology. 2018 May 10;19(1):58.

MetaCon – Unsupervised Clustering of Metagenomic Contigs with Probabilistic k-mers Statistics and Coverage

MetaCon

:: DESCRIPTION

MetaCon is a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage.

::DEVELOPER

Matteo Comin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MetaCon

:: MORE INFORMATION

Citation

BMC Bioinformatics, 20 (Suppl 9), 367 2019 Nov 22
MetaCon: Unsupervised Clustering of Metagenomic Contigs With Probabilistic K-Mers Statistics and Coverage
Jia Qian, Matteo Comin

QCluster – Extending Alignment-free Measures with Quality Values for Reads Clustering

QCluster

:: DESCRIPTION

Qcluster is a software of extending alignment-free measures with quality values for reads clustering.

::DEVELOPER

Matteo Comin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 QCluster

:: MORE INFORMATION

Citation

Clustering of reads with alignment-free measures and quality values.
Comin M, Leoni A, Schimd M.
Algorithms Mol Biol. 2015 Jan 28;10:4. doi: 10.1186/s13015-014-0029-x

MDI-GPU 1.0.1 – Accelerating integrative modelling for Genomic-scale data using GP-GPU Computing

MDI-GPU 1.0.1

:: DESCRIPTION

MDI-GPU is an improved implementation of a Bayesian correlated clustering algorithm, that permits integrated clustering to be routinely performed across multiple datasets, each with tens of thousands of items.

::DEVELOPER

Warwick Systems Biology Centre

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MDI-GPU

:: MORE INFORMATION

Citation

MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing.
Mason SA, Sayyid F, Kirk PD, Starr C, Wild DL.
Stat Appl Genet Mol Biol. 2016 Mar 1;15(1):83-6. doi: 10.1515/sagmb-2015-0055.

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.

IMHRC V1 – Inter-Module Hub Removal Clustering

IMHRC V1

:: DESCRIPTION

IMHRC is a graph clustering algorithm that is developed based on inter-module hub removal in the weighted graphs which can detect overlapped clusters. Due to these properties, it is especially useful for detecting protein complexes in protein-protein interaction (PPI) networks with associated confidence values.

::DEVELOPER

School of Biological Sciences, Iran

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux
  • Java

:: DOWNLOAD

IMHRC

:: MORE INFORMATION

Citation

Sci Rep. 2017 Jun 12;7(1):3247. doi: 10.1038/s41598-017-03268-w.
Discovering overlapped protein complexes from weighted PPI networks by removing inter-module hubs.
Maddi AMA, Eslahchi C