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.

VOGUE 20090520 – Variable Order HMM with Duration

VOGUE 20090520

:: DESCRIPTION

VOGUE is a variable order and gapped HMM with with duration. It uses sequence mining to extract frequent patterns in the data. It then uses these patterns to build a variable order HMM with explicit duration on the gap states, for sequence modeling and classification. VOGUE was applied to model protein sequences, as well as a number of other sequence datasets including weblogs.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 VOGUE

:: MORE INFORMATION

Citation

Mohammed J. Zaki, Christopher D. Carothers and Boleslaw K. Szymanski,
VOGUE: A Variable Order Hidden Markov Model with Duration based on Frequent Sequence Mining.
ACM Transactions on Knowledge Discovery in Data, 4(1):Article 5. Jan 2010

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

PSIST – Protein Indexing

PSIST

:: DESCRIPTION

PSIST (protein structures using suffix trees) uses suffix trees to index protein 3D structure. It first converts the 3D structure into a structure-feature sequence over a new structural alphabet, which is then used to index protein structures. The PSIST index makes it very fast to query for a matching structural fragment.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 PSIST

:: MORE INFORMATION

Citation

Feng Gao and Mohammed J. Zaki,
PSIST: A Scalable Approach to Indexing Protein Structures using Suffix Trees.
Journal of Parallel and Distributed Computing, 68(1):55-63. Jan 2008

ContextShapes – Protein Docking and Partial Shape Matching

ContextShapes

:: DESCRIPTION

ContextShapes does rigid-body protein docking. It uses a novel contextshapes data structure to represent local surface regions/shapes on the protein. All critical points on both the receptor and ligand are represented via context shapes, and the best docking is found via pair-wise matching.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 ContextShapes

:: MORE INFORMATION

Citation

Zujun Shentu, Mohammad Al Hasan, Chris Bystroff and Mohammad J. Zaki,
Context Shapes: Efficient Complementary Shape Matching for Protein-Protein Docking.
Proteins: Structure, Function and Bioinformatics, 70(3):1056-1073. Feb 2008

DWLS – Cell-type Deconvolution using Single-cell RNA-sequencing data

DWLS

:: DESCRIPTION

DWLS (Dampened weighted least squares) is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R

:: DOWNLOAD

DWLS

:: MORE INFORMATION

Citation

Tsoucas D, Dong R, Chen H, Zhu Q, Guo G, Yuan GC.
Accurate estimation of cell-type composition from gene expression data.
Nature Communications. 10 (1), 2975 2019 Jul 5

RESCUE v1.0.3 – Imputing Dropouts in Single-cell RNA-sequencing data

RESCUE v1.0.3

:: DESCRIPTION

RESCUE is a computational method to mitigate the dropout problem by imputing gene expression levels using information from other cells with similar patterns.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python
  • R

:: DOWNLOAD

RESCUE

:: MORE INFORMATION

Citation

BMC Bioinformatics, 20 (1), 388 2019 Jul 12
RESCUE: Imputing Dropout Events in Single-Cell RNA-sequencing Data
Sam Tracy , Guo-Cheng Yuan , Ruben Dries

CUT&RUNTools – A pipeline for analyzing CUT&RUN data

CUT&RUNTools

:: DESCRIPTION

CUTRUNTools contains the pipeline for conducting a CUT&RUN analysis. The pipeline comprises of read trimming, alignment steps, motif finding steps, and finally the motif footprinting step.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

CUTRUNTools

:: MORE INFORMATION

Citation

Genome Biol, 20 (1), 192 2019 Sep 9
CUT&RUNTools: A Flexible Pipeline for CUT&RUN Processing and Footprint Analysis
Qian Zhu , Nan Liu , Stuart H Orkin , Guo-Cheng Yuan

Giotto 1.0.3 – Single-cell Spatial Analysis pipeline

Giotto 1.0.3

:: DESCRIPTION

Giotto is a comprehensive pipeline for spatial transcriptomic data analysis and visualization.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R
  • ImageJ library (JAR file)

:: DOWNLOAD

Giotto

:: MORE INFORMATION

Citation

Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data
Ruben Dries, Qian Zhu, Chee-Huat Linus Eng, Arpan Sarkar, Feng Bao, Rani E George, Nico Pierson, Long Cai, Guo-Cheng Yuan
doi: https://doi.org/10.1101/701680

ECLAIR – Robust Lineage Reconstruction from Single-cell Gene Expression data

ECLAIR

:: DESCRIPTION

ECLAIR (Ensemble Clustering for Lineage Analysis, Inference and Robustness) achieves a higher level of confidence in the estimated lineages through the use of approximation algorithms for consensus clustering and by combining the information from an ensemble of minimum spanning trees so as to come up with an improved, aggregated lineage tree.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

ECLAIR

:: MORE INFORMATION

Citation

Giecold G, Marco E, Trippa L, Yuan GC.
Robust Lineage Reconstruction from High-Dimensional Single-Cell Data.
Nucleic Acids Res. 2016 May 20. pii: gkw452.