Nucleic Acids Research Advance Access published online on May 21, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm302
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
MAGMA: analysis of two-channel microarrays made easy
Hubert Rehrauer*,
Stefan Zoller and
Ralph Schlapbach
Functional Genomics Center Zurich, UZH/ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
*To whom correspondence should be addressed. Tel: +41 44 635 3924; Fax: +41 44 635 3922; Email: Hubert.Rehrauer{at}fgcz.uzh.ch
Received January 30, 2007. Revised March 30, 2007. Accepted April 15, 2007.
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ABSTRACT
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The web application MAGMA provides a simple and intuitive interface
to identify differentially expressed genes from two-channel
microarray data. While the underlying algorithms are not superior
to those of similar web applications, MAGMA is particularly
user friendly and can be used without prior training. The user
interface guides the novice user through the most typical microarray
analysis workflow consisting of data upload, annotation, normalization
and statistical analysis. It automatically generates R-scripts
that document MAGMA's entire data processing steps, thereby
allowing the user to regenerate all results in his local R installation.
The implementation of MAGMA follows the model-view-controller
design pattern that strictly separates the R-based statistical
data processing, the web-representation and the application
logic. This modular design makes the application flexible and
easily extendible by experts in one of the fields: statistical
microarray analysis, web design or software development. State-of-the-art
Java Server Faces technology was used to generate the web interface
and to perform user input processing. MAGMA's object-oriented
modular framework makes it easily extendible and applicable
to other fields and demonstrates that modern Java technology
is also suitable for rather small and concise academic projects.
MAGMA is freely available at
www.magma-fgcz.uzh.ch.
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INTRODUCTION
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Microarrays have become a standard research tool for gene expression
analysis and a number of established solutions exist for the
subsequent data analysis. The R language and the corresponding
Bioconductor packages (
1) have established themselves as the
reference platform for the publication and implementation of
the latest bioinformatic algorithms. The available packages
provide a comprehensive functionality for all aspects of microarray
data analysis. Data preprocessing, normalization, explorative
data mining, differential expression analysis, pathway and GO
analysis can be done as well as even more sophisticated analyses
that elucidate mechanisms of transcriptional regulation, identify
gene networks or perform text mining. However, using the R programming
language requires computing skills that go beyond the skills
of a typical academic life science researcher and microarray
expert. As an alternative, a range of web applications exist
that allow users to perform microarray data analysis with their
web browser. The Bioinformatics Links Directory (
http://bioinformatics.ubc.ca/resources/links_directory)
gives an overview of the available web servers. Intermediate
to experienced users can exploit these applications for the
analysis of their data. However, for a biologist or medical
researcher with no or little experience in microarray analysis,
the majority of these tools are demanding and require too much
prior knowledge about file formats, preprocessing options and
data characteristics to be used right away without any further
assistance.
The web application MAGMA fills this gap by providing a quickstart to two-channel microarray data analysis especially to novice microarray users. It requires only the essential minimum of user input that is needed to be able to run the most frequently used microarray analysis task: the identification of differentially expressed genes. The web interface is specifically designed to be easy and intuitive to use without requiring users to have previous training. Nevertheless, MAGMA performs all the necessary steps to correctly process the data and compute the differentially expressed genes from two-channel microarray data. It is intended to be run directly after the hybridization and image quantification and therefore satisfies the need of many researchers to obtain an instant overview of the amount of differential expression and to get an initial list of regulated genes.
MAGMA's architecture follows the Model-View-Controller design pattern (http://en.wikipedia.org/wiki/Model-view-controller). With this design we achieve a clear separation of the user interface (web pages) from the application logic (Servlets), and the statistical data processing that is delegated to R and Bioconductor. The data processing relies on a framework that models an entire microarray data analysis as a series of atomic processing steps. The results of the individual steps are persistent and offer thus a history that allows users to go back to any previous processing step. Given the modular design of the architecture and the data processing, MAGMA's functionality can be extended by simply implementing additional processing steps without the need to change the code of the core engine or the existing processing steps. The generality of the data processing model also makes the framework suitable for other applications requiring completely different data processing steps.
Examples of other web applications with similar or more extensive microarray analysis functionalities include CARMAweb (2), ArrayQuest (3), Expression Profiler (4), ExpressYourself (5), RACE (6), ArrayPipe (7), GenePublisher (8), SNOMAD (9), GEPAS (10), WebArray (11) and MIDAW (12). Of these applications, only CARMAweb, RACE and GenePublisher generate R-Scripts which allow the user to reproduce the processing on his computer locally. Furthermore, only CARMAweb relies also on a modern Java architecture, however it does not yet make use of the Java Server Faces (JSF) technology which takes from the developers the burden to implement standard GUI functionality. Generally, these web applications aim at providing a comprehensive set of microarray data analysis functionalities. However, to achieve this purpose they compromise on the ease-of-use for first-time users who, instead of a full blown analysis, simply want to get a fast overview over their list of regulated genes. MAGMA on the other hand focuses specifically on this functionality and lets users compute the list of differentially expressed genes without detouring. We assume that users will perform a subsequent in-depth analysis with a dedicated software package (e.g. the academic TM4 suite: http://www.tm4.org/) or a commercial product such as GeneSpring (Agilent), Resolver (Rosetta), Expressionist (Genedata) or similar. MAGMA does, therefore, not intend to replace these applications but complements them by allowing users to carry out a quick initial analysis without the need of correctly filling in detailed LIMS information or setting up gene annotation. In our experience, first-time users are overwhelmed by the huge number of analyses and options these applications offer, and do not get to any result within a reasonable time.
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METHODS
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All data analysis operations in MAGMA are performed using the
R language and Bioconductor packages. By relying on Bioconductor's
limma package (
13), MAGMA accurately models and analyzes the
typical two-group comparisons in reference and dye-swap experimental
designs. In some cases, Bioconductor functions have been extended
with additional error checks because the available functions
would not test appropriately for the validity of the input and
the correctness of the result. All data operations are tracked
in an R-script, making them accessible to the user for documentation
or reuse. Users familiar with R may take the generated R-script,
paste it into their local R installation and reproduce the entire
analysis on their local computer. This could also serve as a
starting point for a subsequent more advanced analysis.
The architecture of MAGMA follows the Model-View-Controller design pattern, and the MAGMA web application is structured accordingly, as shown in Figure 1. The View part defines the HTML pages that are presented to the user. Any user action on the web pages is processed by the Controller who triggers an action in the Model and determines which page is to be displayed subsequently. The values and results that are shown on the HTML pages are directly requested from the model. This functionality separation enforced by the Model-View-Controller pattern enables user interface designers, java developers and statisticians to work independently on the HTML pages, the java part and the statistical data analysis. For the implementation we used the JSF technology (http://java.sun.com/javaee/javaserverfaces, JSF), which provides a library for all user interface elements, and a Controller that is set up with a straight forward XML configuration file. JSF greatly simplifies the generation of web pages and completely takes care of the low level HTTP request processing and user input handling. The data processing is done with R and Bioconductor packages. The R functionality is made available to the java server via Rserve (http://stats.math.uni-augsburg.de/Rserve). The microarray data and processing results are stored in R workspaces. The MAGMA web application can run on any web server that includes a servlet container, e.g. Apache Tomcat (http://tomcat.apache.org/). It is platform independent and has been successfully tested to run under Linux and Windows operating systems.
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Figure 1. Structural representation of MAGMA's architecture. MAGMA implements the Model-View-Controller paradigm and has the HTML pages (View), the rules for the HTML page flow (Controller) and the actual data processing (Model) decoupled. This allows for independent modification or extension of MAGMA.
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MAGMA furthermore has an exception-handling mechanism, which
provides feedback to users if inappropriate data or settings
have been submitted. On the one hand, every input is syntactically
validated at the time of submission, and a notice is displayed
if invalid input is encountered. On the other hand, if unexpected
results occur while processing the data, the processing step
is aborted and an error page is displayed, explaining to the
user the issue encountered, and providing suggestions how to
resolve this.
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PROGRAM DESCRIPTION
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MAGMA provides for each user a separate workspace for storing
and analyzing microarray data. The set of hybridization data
files of a microarray study and their analysis are grouped together
as an experiment within MAGMA. A typical analysis comprises
the four processing steps upload, annotation, normalization
and statistical analysis which are described in more detail
subsequently. Users are intuitively guided through these four
steps by the system. MAGMA has a navigation box (
Figure 2) where
the completed steps of an analysis are represented as distinct
folders. Selecting a step icon links to the corresponding result
page where the result box is shown in the lower area. Further
processing steps can be run by selecting a suitable step from
the Next box. The Manage experiments link is used to switch
between different experiments. Info icons on all pages provide
explanations for the individual boxes. For all input fields
and links, short tool tips show up upon mouse over.
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Figure 2. MAGMA's result navigation panel. For each analyzed experiment, the results are organized in an explorer-like hierarchy. Each folder of the tree represents a single processing step and clicking on it, displays the corresponding result in the lower part of the page. More processing steps can be added after the current processing step by selecting a step from the Next box.
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Each result box has in its title a link to the R-Script that
generated the result. If R and Bioconductor are installed locally,
this R-script can be pasted without any modification in the
local R environment, and will then locally regenerate exactly
the same processing results.
Upload
As input, MAGMA requires data files with raw and processed hybridization intensities from two-channel hybridization experiments. Currently, data files generated by the microarray image quantification applications Axon GenePix, Microdiscovery GeneSpotter and Agilent Feature Extraction software are supported. Further formats will be added on demand. All data files that should be analyzed together have to be packed into a single zip file. In the upload step MAGMA parses the files, computes diagnostic statistics from the extracted values and generates graphs that can be used as a first visual quality control. In particular, MAGMA computes the percentage of negative probes and the percentage of flagged (low-quality signal) probes. This allows the user to determine whether the background intensity and the noise of all hybridizations were acceptable. Additionally, the reported median log ratio of the red and the green intensities indicate for each file whether both channels are well balanced. These statistics are also displayed as graphs (Figure 3) and allow for the easy identification of outliers.
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Figure 3. Graphs visualizing diagnostic statistics. In the example, the percentage of flagged and negative probes as well as the median log ratio is shown for six uploaded arrays. These overview plots allow for the identification of outliers as well as general trends.
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Annotation
In this step, the user should give short and accurate terms
for the experimental conditions of the samples and assign them
to the red and green channels of the data files. This is an
important step, because the subsequent statistical analysis
will use this information to group the data. The boxplots created
in this step show whether the signal range within and across
experimental conditions is consistent.
Normalization
The normalization step is optional, since MAGMA reads data that was already background corrected and normalized by the respective image quantification software. If this pre-normalization was not satisfying, the user can perform an optional background correction and a normalization using a set of methods provided by the Bioconductor package limma. After normalization, the MA-plots are recomputed so that the performance of the normalization can be evaluated visually.
Statistical analysis
Finally, the statistical analysis generates a list of differentially expressed genes between any two of the experimental conditions defined in the annotation step. This list is computed again with the limma package which fits a linear model to the data. The limma package was chosen because it is the only tool that allows for the analysis of all common experimental designs (reference, circular, dye swap) for two-channel data with a simple consistent interface. Corrections for multiple testing can be selected as an advanced option and are computed using Bioconductor's multtest package. The result is displayed as a P-value plot that shows the number of significant genes as a function of the P-value threshold (Figure 4). The genes selected by the current threshold are highlighted in red. For a comparison, the number of significant genes that would be detected in random data is shown in green. The second plot is a volcano plot that shows the negative logarithm of the significance as a function of the logarithm of the fold change (Figure 4). These two plots show the amount of differential expression and the number of genes affected. The result table itself is available as a link and can be saved to any spreadsheet program.
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Figure 4. Result of a statistical analysis that included two comparisons. Each comparison is shown as a row with two plots. The first plot shows the number of significantly differentially expressed genes (red) selected using the current P-value threshold and compares it to results expected from random data. The second figure shows a Volcano plot displaying the P-values and fold changes of all genes with significant genes highlighted in red. The results can be retrieved in tabular form from the links in the rightmost column.
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DISCUSSION
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MAGMA combines Bioconductor's powerful statistical microarray
analysis algorithms with state-of-the-art web application technology
and offers the user the straightforward computation of differentially
expressed genes with his web browser. While MAGMA's processing
algorithms compare to those of other existing web applications,
it stands out through its simple and intuitive usage and its
comprehensive exception handling system, and therefore specifically
aims at allowing novice users to analyze their data without
prior training. MAGMA automatically generates R-scripts that
guarantee the reproducibility of the generated results and thus
allows advanced users to further extend the analysis. To our
knowledge, it is the only web application where the generated
R-script can be pasted without any modification into a local
R/Bioconductor environment in order to reproduce all microarray
data analysis results locally.
On the technical side, MAGMA demonstrates that modern software technologies, like the JSF framework, can be successfully applied even in small and concise academic projects and leads to well-structured solutions. The benefit of the chosen Java-based approach is several-fold:
- Java is platform independent and runs on all major operating systems.
- JSF technology comes with a comprehensive set of standard functionalities for web applications and simplifies the user interface development.
- With Eclipse (www.eclipse.org), a freely available prime-class integrated development environment exists that supports collaborative software development, automatic code generation, instant syntax checking, source code versioning and integrated testing and deployment of web applications.
- Java's object orientation intrinsically suggests the implementation of modular software packages that are easy to maintain and extend.
The MAGMA framework has already proven its flexibility and extensibility, as we have reused it to implement a dedicated web-based processing pipeline for the microarray data generated by the EuReGene project (www.euregene.org), with one person working less than a week on it.
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ACKNOWLEDGEMENTS
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We would like to thank for the critical feedback of many users
in the beginning of the project. We thank further Christian
Ahrens, Andrea Patrignani, Ulrich Wagner and Marzanna Künzli
for discussions and comments on the manuscript. Many thanks
also go to Snowflake Productions, Switzerland for the nice design
of the web pages. Funding to pay the Open Access publication
charges for this article was provided by the Functional Genomics
Center Zurich.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION