Tag: Microarray

SNOMAD – Standardization & Normalization of Microarray Data

SNOMAD

:: DESCRIPTION

SNOMAD is a program for the standardization and normalization of gene expression datasets. In addition to conventional transformations and visualization tools, SNOMAD includes two non-linear transformations which correct for bias and variance which are non-uniformly distributed across the range of microarray element signal intensities: local mean normalization and local variance correction (Z-score generation using a locally calculated standard deviation).

::DEVELOPER

the Pevsner Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R package

:: DOWNLOAD

 SNOMAD

:: MORE INFORMATION

Citation

Bioinformatics. 2002 Nov;18(11):1540-1.
SNOMAD (Standardization and NOrmalization of MicroArray Data): web-accessible gene expression data analysis.
Colantuoni C, Henry G, Zeger S, Pevsner J.

AnovArray 1.1 – SAS macros for Analysing Microarray- and Macroarray-type Expressional data

AnovArray 1.1

:: DESCRIPTION

AnovArray is a set of SAS subroutines for analysing microarray- and macroarray-type expressional data. It quantifies biological and technological variation sources and detects differentially expressed genes between several conditions. Statistical methods used are analysis of variance (ANOVA) and FDR method (False Discovery Rate) for calculating probabilities adjusted in a multiple hypotheses test framework.

::DEVELOPER

Karine Piot

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  AnovArray

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2005 Jun 16;6:150.
AnovArray: a set of SAS macros for the analysis of variance of gene expression data.
Hennequet-Antier C, Chiapello H, Piot K, Degrelle S, Hue I, Renard JP, Rodolphe F, Robin S.

IBMT – Testing for Differentially Expressed Genes in Microarrays

IBMT

:: DESCRIPTION

IBMT is a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic.The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

:: DEVELOPER

Laboratory for Statistical Genomics, Univ. Cincinnati

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • R Package

:: DOWNLOAD

 IBMT

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2006 Dec 19;7:538.
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments.
Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M.

MBR 20070206 – Microarray Blob Remover

MBR 20070206

:: DESCRIPTION

MBR (Microarray Blob Remover) is a microarray JAVA tool which allows rapid visualization, detection, and removalof blob-like defects as an initial quality control step. The software allows rapid visualization, detection, and removal of blob defects of a variety of sizes and shapes from different types of microarrays using their .CEL files. Removal of the affected probes in the blob-defects using MBR was shown to significantly improve sensitivity and FDR compared to leaving the affected probes in the analysis.

::DEVELOPER

Xiaole Shirley Liu Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Mac OsX / Windows
  • Java

:: DOWNLOAD

 MBR

:: MORE INFORMATION

Citation:

Song JS, Maghsoudi K, Li W, Fox E, Quackenbush J, Shirley Liu X.
Microarray blob-defect removal improves array analysis.
Bioinformatics. 2007 Apr 15;23(8):966-71. Epub 2007 Mar 1.

ChipInspector 2.1 – Microarray Data Analysis

ChipInspector 2.1

:: DESCRIPTION

ChipInspector is Genomatix tool for high quality microarray data analysis. Based on single probes instead of probe sets results of superior significance can be retrieved in combination with the Genomatix genome annotation. Expression levels can be assigned not only to genes but to separate (alternative) transcripts.

ChipInspector analyzes raw data files from microarray experiments. It selects those probes from the chip, where the signal is significantly different from the background and puts them in their biological context.

ChipInspector works with data from exon-, tiling- or gene expression-arrays from vendors like Affymetrix, Illumina or Agilent.

::DEVELOPER

Genomatix Software GmbH

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

ChipInspector

:: MORE INFORMATION

VAMPIRE – Interpret Microarray Gene Expression Data

VAMPIRE

:: DESCRIPTION

VAMPIRE (variance-modeled posterior inference with regional exponentials) was originally developed to interpret one-channel microarray data, such as Affymetrix oligonucleotide arrays. Given a summary measure of gene expression, such as the Affymetrix MAS 5.0 scores for each microarray feature (or probe set), it determines the optimal variance model parameters for a two-component variance model. The expression-independent variance represents a constant “background” noise that affects all array features to the same extent, while the expression-dependent variance represents a proportional noise that increases with gene expression. Low-intensity features thus have larger proportional of noise, because of the influence of expression-independent variance. With this optimized model, VAMPIRE then computes a Bayesian statistical test to determine whether observed changes in intensity are statistically significant.

::DEVELOPER

Subramaniam Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /MacOsX
  • Java

:: DOWNLOAD

 VAMPIRE

:: MORE INFORMATION

Citation

Albert Hsiao, Trey Ideker, Jerrold M. Olefsky and Shankar Subramaniam
VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data
Nucleic Acids Research 33 (suppl 2): W627-W632.

ITACA 1.0 – Integrative Tool for microArray and CESH Analysis

ITACA 1.0

:: DESCRIPTION

ITACA is an application that enables simultaneous visualization and manipulation of microarray data (MA), Affymetrix microarray data (AFFY) and comparative expressed sequence hybridization data (CESH) in an intuitive graphical way. The application can be used for analyzing the human and mouse data in order to detect the differentially expressed genes, useful tools in identification of candidate genes involved in different biological processes or diseases.

::DEVELOPER

Genomics & Bioinformatics Graz, Graz University of Technology

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 ITACA

:: MORE INFORMATION

CARMAweb 1.3 – R- and Bioconductor-based web service for Microarray Data Analysis

CARMAweb 1.3

:: DESCRIPTION

CARMAweb (Comprehensive R-based Microarray Analysis web service) is a web application designed for the analysis of microarray data. CARMAweb performs data preprocessing (background correction, quality control and normalization), detection of differentially expressed genes, cluster analysis, dimension reduction and visualization, classification, and Gene Ontology-term analysis. This web application accepts raw data from a variety of imaging software tools for the most widely used microarray platforms: Affymetrix GeneChips, spotted two-color microarrays and Applied Biosystems (ABI) microarrays.

::DEVELOPER

Genomics & Bioinformatics Graz, Graz University of Technology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CARMAweb

:: MORE INFORMATION

Citation

Rainer J., Sanchez-Cabo F., Stocker G., Sturn A. and Trajanoski Z.
CARMAweb: comprehensive R- and Bioconductor-based web service for microarray data analysis
Nucleic Acids Research, 2006 vol 34(Web Server Issue):W498-503.

J-Express 2012 – Analysis and Visualization of Microarray Data

J-Express 2012

:: DESCRIPTION

J-Express is a comprehensive portable software package for analysis and visualization of microarray data. The software gives access to methods for unsupervised analysis (clustering etc.), supervised analysis (SAM, Feature Subset Selection, etc.) and visualisation in an integrated and flexible way. Its efficiency allows interactive clustering of thousands of expression profiles on standard personal computers. Supervised analysis can be performed on samples by simple user-guided sample annotation or on genes through tools for metabolic pathway analysis and Gene Ontology mapping.

J-Express contains many analysis methods and data preparation tools to make the most of your data. Image analysis files can be easily prepared (normalized etc.) and combined for downstream analysis. Supervised methods can discover patterns in the data and reveal genes or gene groups (such as gene ontology groups or metabolic pathways) differentially expressed between samples. unsupervised methods can organize the data and discover patterns, or reveal underlying biological functions such as co-regulation.

Features

  • New analysis methods
  • Genset enrichment analysis (GSEA)
  • Chromosome (DNA sequence) mapping and analysis
  • Gaussian kernels
  • Cross-data class prediction
  • Linear and symmetric color table support
  • Faster dataset repository access
  • Simple embedded resource download
  • Native look and feel

::DEVELOPER

Molmine AS

:: SCREENSHOTS

:: REQUIREMENTS

  • Multiplatform
  • Java 6.0 runtime environment.(see www.java.com)

:: DOWNLOAD

J-Express

:: MORE INFORMATION

Most of the advanced features in J-Express are locked when no license is applied. Read more about License package details. Purchase J-Express Licenses.

L2L 1.2 – Microarray Analysis Tool

L2L 1.2

:: DESCRIPTION

 L2L is a simple but powerful tool for discovering the hidden biological significance in microarray data. Through an easy-to-use web interface, L2L will mine a list of up- or down-regulated genes for Gene Ontology terms that are significantly enriched. L2L can also compare the list of genes to a database of hundreds of published microarray experiments, in order to identify common patterns of gene regulation. A downloadable command-line version can run customized and batch analyses.

::DEVELOPER

John Newman

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /MacOsX
  • perl 

:: DOWNLOAD

 L2L

:: MORE INFORMATION

Citation

John C Newman and Alan M Weiner
L2L: a simple tool for discovering the hidden significance in microarray expression data
Genome Biology 2005, 6:R81

 

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