Background

A number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE.

Methods

In this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set.

Results

Differentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignature to a fifth independent data set.

Conclusions

The novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature.

Abstract Background The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip® microarrays unprocessed before their manufacturer’s expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions. Results Microarray data were generated in 2009 in three replicates for each of the two MAQC samples with either expired Affymetrix U133A or unexpired U133Plus2 microarrays. These results were compared with data obtained in 2005 on the U133Plus2 microarray. The percentage of overlap between the lists of differentially expressed genes (DEGs) from U133Plus2 microarray data generated in 2009 and in 2005 was 97.44%. While there was some degree of fold change compression in the expired U133A microarrays, the percentage of overlap between the lists of DEGs from the expired and unexpired microarrays was as high as 96.99%. Moreover, the microarray data generated using the expired U133A microarrays in 2009 were highly concordant with microarray and TaqMan® data generated by the MAQC project in 2005. Conclusions Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturer’s expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the MAQC reference RNA samples, stored at -80°C, were stable over a time frame of at least four years.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.