dictyExpress 1.5 – Dictyostelium Gene Expression Analysis

dictyExpress 1.5

:: DESCRIPTION

dictyExpress is an interactive, web-based exploratory data analytics application providing access to over 1,000 Dictyostelium gene expression experiments from Baylor College of Medicine. The applications consists of components for data retrieval, selection of individual genes or groups of genes, graphic display of gene expression time courses, Gene Ontology term enrichment analysis, display of gene co-expression networks, hierarchical clustering, and expression profile visualization of selected genes in different experiments. The components are connected such that a change in any one of the components (e.g., selection of a gene subset from the hierarchical clustering dendrogram) can propagate to other components and their associated visualizations.

::DEVELOPER

BioLab , University of Ljubljana

:: SCREENSHOTS

:: REQUIREMENTS

  • Any Web Browser

:: DOWNLOAD

 dictyExpress

:: MORE INFORMATION

Citation:

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface.
Rot G, Parikh A, Curk T, Kuspa A, Shaulsky G, Zupan B.
BMC Bioinformatics. 2009 Aug 25;10:265.

EDGE 2.22.0 – Extraction of Differential Gene Expression

EDGE 2.22.0

:: DESCRIPTION

EDGE is a software package for the significance analysis of DNA microarray experiments for both standard and time course experiments based on our new Optimal Discovery Procedure and Time Course Methodology

::DEVELOPER

Jeffrey Leek, Alan Dabney, Eva Monsen and John Storey.

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • R
  • BioConductor

:: DOWNLOAD

EDGE

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Feb 15;27(4):509-15. doi: 10.1093/bioinformatics/btq701. Epub 2010 Dec 24.
A computationally efficient modular optimal discovery procedure.
Woo S, Leek JT, Storey JD.

GEN3VA – Gene Expression and Enrichment Vector Analyzer

GEN3VA

:: DESCRIPTION

GEN3VA is a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO.

::DEVELOPER

Ma’ayan Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

GEN3VA

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2016 Nov 15;17(1):461.
GEN3VA: aggregation and analysis of gene expression signatures from related studies.
Gundersen GW, Jagodnik KM, Woodland H, Fernandez NF, Sani K, Dohlman AB, Ung PM, Monteiro CD, Schlessinger A, Ma’ayan A

dChip 2011.12 – Analysis & Visualization of Gene Expression & SNP Microarrays

dChip 2011.12

:: DESCRIPTION

DNA-Chip Analyzer (dChip) is a Windows software package for probe-level (e.g. Affymetrix platform) and high-level analysis of gene expression microarrays and SNP microarrays.

Gene expression or SNP data from various microarray platforms can also be analyzed by importing as external dataset. At the probe level, dChip can display and normalize the CEL files, and the model-based approach allows pooling information across multiple arrays and automatic probe selection to handle cross-hybridization and image contamination. High-level analysis in dChip includes comparing samples, hierarchical clustering, view expression and SNP data along chromosome, LOH and copy number analysis of SNP arrays, and linkage analysis. In these functions the gene information and sample information are correlated with the analysis results.

::DEVELOPER

Started in Wing Wong Lab , Developed & Maintained by Cheng Li Lab.

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Linux with Wine
  • Mac with Virtual PC

:: DOWNLOAD

dChip

:: MORE INFORMATION

Please cite Li and Wong 2001a if dChip results are used in manuscripts, and cite Lin et al. 2004 if dChip SNP analysis functions are used.

consensusOV 1.12.0 – Gene Expression-based Subtype Classification for High-grade Serous Ovarian Cancer

consensusOV 1.12.0

:: DESCRIPTION

consensusOV implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.

::DEVELOPER

Princess Margaret Bioinformatics and Computational Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

  consensusOV

:: MORE INFORMATION

Citation:

Chen GM, Kannan L, Geistlinger L, Kofia V, Waldron L, Haibe-Kains B (2018).
consensusOV: Gene expression-based subtype classification for high-grade serous ovarian cancer. R package version 1.4.1,
http://www.pmgenomics.ca/bhklab/software/consensusOV

Genefu 2.23.1 – Computation of Gene Expression-Based Signatures in Breast Cancer

Genefu 2.23.1

:: DESCRIPTION

Genefu contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis.

::DEVELOPER

Princess Margaret Bioinformatics and Computational Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R/Bioconductor

:: DOWNLOAD

 Genefu

:: MORE INFORMATION

Citation:

Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer.
Gendoo DM, Ratanasirigulchai N, Schröder MS, Paré L, Parker JS, Prat A, Haibe-Kains B.
Bioinformatics. 2015 Nov 24. pii: btv693.

KGGSEE 1.0 – Biological Knowledge-based Mining Platform for Genomic and Genetic association Summary statistics using gEne Expression

KGGSEE 1.0

:: DESCRIPTION

KGGSEE is a standalone Java tool for knowledge-based secondary analyses of genomic and genetic association summary statistics of complex phenotypes by integrating gene expression and related data. It has four major integrative analyses, 1) unconditional gene-based association guided by expression quantitative trait loci (eQTLs), 2) conditional gene-based association guided by selective expression in tissues or cell types, 3) estimation of phenotype-associated tissues or cell-type based on gene expression in single-cell or bulk cells of different tissues, and 4) causal gene inference for complex diseases and/or traits based-on multiple eQTL.

::DEVELOPER

Precision Medicine Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Java

:: DOWNLOAD

KGGSEE

:: MORE INFORMATION

Citation

1. Xue C., et al. A global overview of single-cell type selectivity and pleiotropy in complex diseases and traits. In Submission (For estimation of phenotype-associated tissues or cell-type based on gene expression in single-cell or bulk cells of different tissues)

2. Jiang L., et al. Systematic comparative analysis of Mendelian randomization methods for inferring causal genes of complex phenotypes and the application to psychiatric diseases. In Submission (For causal gene inference for complex diseases and/or traits based-on multiple eQTL)

3. Li X.Y., et al. Gene-based association guided by eQTL. In Submission (For unconditional and condition gene-based association guided by eQTL)

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.

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

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.