rankSynergy – Rank-based Statistical Test for measuring Synergistic Effects between two Gene Sets

rankSynergy

:: DESCRIPTION

The rankSynergy performs a rank-based non-parametric statistical test for measuring the synergistic effects between two gene sets. For calculating an approximate significance value of synergy, an efficient Markov chain Monte Carlo method is used

::DEVELOPER

Yuichi Shiraishi (friend1ws@gmail.com)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
C++ Compiler

:: DOWNLOAD

rankSynergy

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Sep 1;27(17):2399-405. doi: 10.1093/bioinformatics/btr382.
A rank-based statistical test for measuring synergistic effects between two gene sets.
Shiraishi Y, Okada-Hatakeyama M, Miyano S.

gGranger 1.0.0 – Identification of Granger Causality between Gene Sets

gGranger 1.0.0

:: DESCRIPTION

gGranger performs Granger causality test between sets of time series using bootstrap or likelihood ratio test with Bartlett correction

::DEVELOPER

Andre Fujita

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
R package

:: DOWNLOAD

gGranger

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Sep 15;26(18):2349-51. doi: 10.1093/bioinformatics/btq427.
A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets.
Fujita A, Kojima K, Patriota AG, Sato JR, Severino P, Miyano S.

FuncAssociate 2.0 – Gene Sets Characterizing & Functional Trend Analysis

FuncAssociate 2.0

:: DESCRIPTION

FuncAssociate is a web application that discovers properties enriched in lists of genes or proteins that emerge from large-scale experimentation. FuncAssociate takes a list of genes as input and produces a ranked list of the Gene Ontology terms enriched in an input list.

::DEVELOPER

Roth Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX
Python/Perl

:: DOWNLOAD

FuncAssociate

:: MORE INFORMATION

Citation

Bioinformatics. 2009 Nov 15;25(22):3043-4. doi: 10.1093/bioinformatics/btp498. Epub 2009 Aug 28.
Next generation software for functional trend analysis.
Berriz GF, Beaver JE, Cenik C, Tasan M, Roth FP.

GSCA 1.1.1 – Identification of Differentially Co-expressed Gene Sets

GSCA 1.1.1

:: DESCRIPTION

GSCA (Gene Set Co-Expression Analysis) is a sofware to identify differentially co-expressed gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes.

::DEVELOPER

Kendziorski Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOsX
R package

:: DOWNLOAD

GSCA

:: MORE INFORMATION

Citation

Bioinformatics. 2009 Nov 1;25(21):2780-6. doi: 10.1093/bioinformatics/btp502. Epub 2009 Aug 18.
Statistical methods for gene set co-expression analysis.
Choi Y, Kendziorski C.