Microarray Data Analysis Toolbox (MDAT): for normalization, adjustment and analysis of gene expression data (original) (raw)
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ArrayNorm: comprehensive normalization and analysis of microarray data
Bioinformatics/computer Applications in The Biosciences - BIOINFORMATICS, 2004
ArrayNorm is a user-friendly, versatile and platform independent Java application for the visualization, normalization and analysis of two-color microarray data. A variety of normalization options were implemented to remove the systematic and random errors in the data, taking into account the experimental design and the particularities of every slide. In addition, Ar-rayNorm provides a module for statistical identification of genes with significant changes in expression. Availability: The package is freely available for academic and non-profit institutions from
Bioinformatics, 2002
Summary: SNOMAD is a collection of algorithms for the normalization and standardization of gene expression datasets derived from diverse biological and technological sources. 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: (1) Local mean normalization; and (2) Local variance correction (Z-score generation using a locally calculated standard deviation). Availability: The SNOMAD tools were developed in the R statistical language http://www.r-project.org/. SNOMAD is an interactive, user-friendly web-application which can be accessed freely via the internet with any standard HTML browser: http://pevsnerlab.kennedykrieger.org/snomad.htm. Contact: ccolantu@jhmi.edu or pevsner@jhmi.edu * To whom correspondence should be addressed. † Present address: Wadham College, Oxford University, UK.
Robust Local Normalization Of Gene Expression Microarray Data
2000
analysis of expression microarray data is normalization: the process of adjusting the signal from 2 different reporter channels on a single microarray or a single-reporter channel on multiple microarrays to a common scale. Current methods involve either normalization to some representative statistic of all of the data or to a statistic of some subset of the data, such as a set of "housekeeping" genes. The first method fails to correct any non-linearity between the data channels; the second method is sometimes undone by differential expression of genes that were thought to be unregulated. Agilent has invented a normalization method that uses robust statistical methods to establish the "central tendency" of a set of differential expression data. Normalization utilizes the data clustered near this central tendency; these points comprise an experimentally determined set of housekeeping genes. The resulting algorithm is rapid, robust and capable of correctly normalizing microarray data from different platforms, such as cDNA and in situ synthesized oligonucleotide microarrays. In addition, the method provides an easily interpreted measurement of the degree to which the normalization has altered the original data.
Microarray probe expression measures, data normalization and statistical validation
Comparative and functional genomics, 2003
DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.
Acta biochimica Polonica, 2011
Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory resu...
Genetics Selection Evolution, 2007
Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Article published by EDP Sciences and available at http://www.gse-journal.org or http://dx.doi.org/10.1051/gse:2007031 670 M. Watson et al.
ANDROMEDA: A MATLAB automated cDNA microarray data analysis platform
IFIP International Federation for Information Processing, 2007
DNA microarrays constitute a relatively new biological technology which allows gene expression profiling at a global level by measuring mRNA abundance. However, the grand complexity characterizing a microarray experiment entails the development of computationally powerful tools apt for probing the biological problem studied. ANDROMEDA (Automated aND RObust Microarray Experiment Data Analysis) is a MATLAB implemented program which performs all steps of typical microarray data analysis including noise filtering processes, background correction, data normalization, statistical selection of differentially expressed genes based on parametric or non parametric statistics and hierarchical cluster analysis resulting in detailed lists of differentially expressed genes and formed clusters through a strictly defined automated workflow. Along with the completely automated procedure, ANDROMEDA offers a variety of visualization options (MA plots, boxplots, clustering images etc). Emphasis is given to the output data format which contains a substantial amount of useful information and can be easily imported in a spreadsheet supporting software or incorporated in a relational database for fiirther processing and data mining.
Handbook of Molecular and Cellular Methods in Biology and Medicine, Third Edition, 2011
Gene expression profiling has revolutionized functional genomics research by providing a quick handle on all the transcriptional changes that occur in the cell in response to internal or external perturbations or developmental programs. Microarrays have become the most popular technology for recording gene expression profiles. This chapter describes all the necessary steps for analyzing Affymetrix microarray data using the open-source statistical tools (R and bioconductor). The reader is walked through all the basic steps of data analysis: reading raw data, assessing quality, preprocessing/normalization, discovery of differentially expressed genes, comparison of gene lists, functional enrichment analysis, and saving results to files for future reference. Some familiarity with computer is assumed. This chapter is self-contained with installation instructions for R and bioconductor packages along with links to downloadable data and code for reproducing the examples.
EMA - A R package for Easy Microarray data analysis
BMC Research Notes, 2010
Background: The increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users. Findings: Based on the experience of biostatisticians of Institut Curie, we propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results. Conclusions: Strategy and tools proposed in the EMA R package could provide a useful starting point for many microarrays users. EMA is part of Comprehensive R Archive Network and is freely available at http://bioinfo.curie.fr/ projects/ema/.