Introduction

NuGO (http://www.nugo.org) provides, to each partner, a local NuGO Black Box (NBX: a powerful computer to, e.g., store (nutri-BASE: http://www.ebi.ac.uk/~oyeniran/) and analyze (GenePattern: http://www.broad.mit.edu/cancer/software/GenePattern/) [6] NuGO and Affymetrix microarrays. NuGO arrays are custom made Affymetrix arrays. NuGO defined the probes on these chips, but kept the same design and setup as seen on standard Affymetrix expression arrays. The NuGO technology work package has put much effort into helping NuGO members to analyze their “omics” data. In this paper, we focus on the GenePattern modules created by NuGO to help NuGO scientists with their microarray analyses.

GenePattern, provided by the Broad institute, is a simple interface to a large number of analytic tools for genomics data. Modules are written in Java, MATLAB, Perl, or R/Bioconductor. The user friendly graphical interface allows biologist to easily enter their data and choose suitable settings in order to perform complex analyses (quality control, statistics, gene enrichment analysis, and so on), without detailed knowledge of the underlying programming language, algorithms and settings, allowing them to concentrate their efforts on interpretation of biologically meaningful results. A GenePattern user can run individual modules or create a pipeline, combining the use of various modules. A typical GenePattern pipeline could look as depicted in Fig. 1.

Fig. 1
figure 1

An example of a GenePattern workflow. Before performing statistics on a microarray experiment, it must be verified whether the array quality is acceptable

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GenePattern managers can build their own modules. In the next paragraphs, we will present some modules, created by NuGO, which allow complex and easy to use analysis of affymetrix microarray data in GenePattern.

NuGOArrayQualityAnalysis

As depicted in Fig. 1, all microarray analysis should start with assessing the quality of your microarrays. Only if you are sure that the expression values derived from the arrays reflect your experimental setting (answers your scientific question), you can safely continue with the statistical analyses described below. The module takes the .CEL-files, the typical output format of GCOS affymetrix software where intensity calculations on the pixel values of the .dat files (the raw image data from affymetrix chip scanner) are stored (packed in a single zip-file), and submits them to a dedicated NuGO R-server via web services (written in Perl), executes the quality control pipeline utilizing a Bioconductor script, and returns the result as a single zip-file. The quality control procedure is identical to the implementation in MADMAX [3] (https://madmax.bioinformatics.nl) and the web services are just computer protocols to handle file transfers (via secure http) to execute the Bioconductor calculations, and to return the results (http://nugo-r.bioinformatics.nl/MADMAX_services/MADMAX_services.html) [5].

NuGOExpressionFileCreator

Utilizing GenePattern requires a function to import (and normalize) the data in such a way that all other modules can work with it. NuGOExpressionFileCreator is an enhanced version from the standard ExpressionFileCreator module that is present in GenePattern. It uses the most up-to-date Bioconductor version and supports the MBNI Custom CDF-files (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/genomic_curated_CDF.asp) [2]. NuGO provides an up-to-date R and Bioconductor installation for the NBXses including annotation libraries for many Custom CDF-files (http://nugo-r.bioinformatics.nl/NuGO_R.html) and support libraries for the NuGO arrays. The NuGO GenePattern installation depends on these Ubuntu (.deb) R and Bioconductor packages. Consequently, NuGOExpressionFileCreator can create a so-called .chip-file that is specific for the utilized custom CDF-file. The .chip-file is required for properly executing the Gene Set Enrichment Analysis (GSEA: http://www.broad.mit.edu/gsea/) [7] later on in GenePattern (if the biologist wants to).

Figure 2 shows the input as a zip-file containing the .CEL-files and a so-called .clm file that defines the groups that the .CEL-files belong to (GenePattern help describes the proper file format). Some settings, e.g., normalization method, are required but the default values are usually convenient. Typically, the user loads the zip-file and the .clm-file and clicks on the “run” button. The output files are a .gct file and a .cls file: common GenePattern input files that can be used by many modules.

Fig. 2
figure 2

A screenshot of the NuGOExpressionFileCreator module at initialization of a run

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LimmaAnalysis

The NuGO GenePattern module LimmaAnalysis implements the bioconductor package Limma (available at http://www.bioconductor.org/packages/release/bioc/html/limma.html). The Limma package uses moderated t and F statistics based on linear modelling in order to perform differential gene expression analysis for data arising from microarray experiments. The main advantage of Limma over traditional t or F tests is that for the estimation of variances and standard errors of a single gene information is borrowed from other genes, which stabilizes the analysis particularly for small sample sizes [8, 9]. The GenePattern module LimmaAnalysis provides an easy to use interface, allowing Limma analysis to be performed by researchers unfamiliar with the R and Bioconductor programming environment. At present, the LimmaAnalysis module can handle one-factorial experimental designs with a potential additional blocking factor. This covers many relevant study designs in nutritional research as for example the comparison of different dietary treatments on the same subject. The module takes the .gct file and .cls file obtained from NuGOExpressionFileCreator as input and returns the Limma results table including P values, Benjamini-Hochberg corrected P values and average log-ratios for all possible pairwise comparisons in separate files. The result tables give scientists an overview about which genes are significantly differentially expressed but they can also be used for further analysis with, e.g., the NuGO modules TopGoAnalysis and GetResultForGO.

TopGoAnalysis and GetResultForGO

The GenePattern module TopGoAnalysis implements the Bioconductor package topGO (available at http://bioconductor.org/packages/release/bioc/html/topGO.html). The Bioconductor module topGO is a gene enrichment analysis tool, which integrates the knowledge about the relationship between GO terms for the calculation of statistical significance. It allows to choose from three methods for Gene Ontology term scoring: Classic Method (classic), Eliminating Genes method (elim), or Weighting Genes method (weight) and the application of two test statistics: Fisher exact test (FIS) [4] or Kolmogorov–Smirnov test (KS) [7]. For further details on the topGO Bioconductor package see [1]. The GenePattern module TopGoAnalysis provides an intuitive user interface and implements five tests, combining the three methods for Gene Ontology term scoring with the two test statistics. For each test it returns the results table of the top 100 enriched GO identifiers plus a .pdf file containing the GO-graph for each test performed (Table 1).

Table 1 An overview of the utilized GO test statistics combined with the GO terms scoring
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TopGoAnalysis is a quick way to gain information about the main GO molecular functions (MF), biological processes (BP), or cellular components (CC) affected by any treatments applied in nutritional research. This information is useful as a guide for the design of follow up experiments, because if, for example, a nutritional treatment affects many genes associated with a particular MF, BP, or CC this is likely to be much more relevant compared to any effects observed on single genes. To provide help with the design of follow-up experiments, any GO identifiers of interest can be further investigated with the GenePattern module GetResultForGO. This module utilizes an R script, which allows users to filter the LimmaAnalysis results table for genes annotated with a particular GO identifier of interest. This is useful for, e.g., examining whether genes associated with a particular GO identifier are predominantly up or down regulated in response to a nutritional or other intervention. It also allows the identification of those genes associated with a particular GO identifier, which responded most strongly to a particular treatment to help determine the most suitable target genes for validation of microarray data with real-time PCR.

GSEA and GSEALeadingEdgeViewer

These modules are already useful in the form provided in GenePattern. For GSEA, the biologist should provide, next to the .gct and .cls files, the proper .chip-file (provided by NuGOExpressionFileCreator). After finishing the GSEA analysis, the GSEALeadingEdgeViewer can be started to more thoroughly examine the GSEA results.

Conclusion

NuGO provides a number of user-friendly tools that implement the most relevant and up-to-date Bioconductor modules with respect to Affymetrix microarray analysis, which biologists can run in GenePattern, without knowledge of the R programming language. This approach allows biologists to focus on the underlying biology. Please note that GenePattern provides modules not only for microarray, but also for SNP, proteomics, and sequence analysis.