SpeCond 1.22.0 – Detect Condition-specific Gene Expression

SpeCond 1.22.0

:: DESCRIPTION

SpeCond performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions.

::DEVELOPER

Florence Cavalli <florence at ebi.ac.uk>

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux/Windows/MacOsX
  • R
  • Bioconductor

:: DOWNLOAD

 SpeCond

:: MORE INFORMATION

Citation

Genome Biol. 2011 Oct 18;12(10):R101. doi: 10.1186/gb-2011-12-10-r101.
SpeCond: a method to detect condition-specific gene expression.
Cavalli FM1, Bourgon R, Vaquerizas JM, Luscombe NM.

WMAXC 20131115 – Weighted MAXimum Clique method for Identifying Condition Specific Sub-network

WMAXC 20131115

:: DESCRIPTION

WMAXC reveals a subset of genes which are closely related to a particular disease. It integrates gene expression data and protein-protein interaction information to construct molecular network, and then extracts the most density connected sub-network using an integration of a global search method and efficient projection procedure.

::DEVELOPER

Data Mining & Computational Biology (DCB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • MatLab

:: DOWNLOAD

 WMAXC

:: MORE INFORMATION

Citation

PLoS One. 2014 Aug 22;9(8):e104993. doi: 10.1371/journal.pone.0104993. eCollection 2014.
WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
Amgalan B, Lee H.

LIGAP 20130505 – Identify Condition/Lineage Specific Time-course Profiles

LIGAP 20130505

:: DESCRIPTION

LIGAP allows integrative analysis and visualization of multiple lineages over whole time-course profiles.

::DEVELOPER

Tarmo Äijö

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Matlab

:: DOWNLOAD

 LIGAP

:: MORE INFORMATION

Citation

BMC Genomics. 2012 Oct 30;13:572. doi: 10.1186/1471-2164-13-572.
An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation.
Aijö T, Edelman SM, Lönnberg T, Larjo A, Kallionpää H, Tuomela S, Engström E, Lahesmaa R, Lähdesmäki H.

EB-HMM 1.0-1 – Identification of Genes Differentially Expressed across 2 or more Conditions over Time

EB-HMM 1.0-1

:: DESCRIPTION

EB-HMM is a tools for comparing multiple biological conditions with time course microarray data using the Hidden Markov modeling

::DEVELOPER

Kendziorski Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • R package

:: DOWNLOAD

 EB-HMM

:: MORE INFORMATION

Citation

Yuan and Kendziorski,
Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions
Journal of the American Statistical Association 101(476): 1323-1332;