AMDA: an R package for the automated microarray data analysis

BACKGROUND: Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. Microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users. RESULTS: To address these problems we have developed an automated microarray data analysis (AMDA) software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation. CONCLUSION: AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopoli

Interleukin-2 Production by Dendritic Cells and its Immuno-Regulatory Functions

Dendritic cells (DCs) are uniquely potent antigen presenting cells that acquire microbial products and prime adaptive immune responses against pathogens. Furthermore, DCs also play a key role in induction and maintenance of tolerance. Although numerous studies have assessed the diverse functions of DCs, many unanswered questions remain regarding the molecular mechanisms that DCs use to achieve immunoregulation. While not widely regarded as a significant provider of T-cell growth factors, DCs have previously been identified as a potential source of IL-2 cytokine. Recent research indicates that microbes are the most effective stimuli to trigger IL-2 production in DCs by activating the calcineurin/NFAT signaling pathway. Herein we describe recent insights into the production and function of IL-2 cytokine and IL-2 receptor in DCs early after stimulation through pattern recognition receptors. These findings clarify how DCs fine-tune effector and regulatory responses by modulating IL-2 production in both tolerance and immunity

Gene Expression Profiles Identify Inflammatory Signatures in Dendritic Cells

Dendritic cells (DCs) constitute a heterogeneous group of antigen-presenting leukocytes important in activation of both innate and adaptive immunity. We studied the gene expression patterns of DCs incubated with reagents inducing their activation or inhibition. Total RNA was isolated from DCs and gene expression profiling was performed with oligonucleotide microarrays. Using a supervised learning algorithm based on Random Forest, we generated a molecular signature of inflammation from a training set of 77 samples. We then validated this molecular signature in a testing set of 38 samples. Supervised analysis identified a set of 44 genes that distinguished very accurately between inflammatory and non inflammatory samples. The diagnostic performance of the signature genes was assessed against an independent set of samples, by qRT-PCR. Our findings suggest that the gene expression signature of DCs can provide a molecular classification for use in the selection of anti-inflammatory or adjuvant molecules with specific effects on DC activity

Classification of dendritic cell phenotypes from gene expression data

<p>Abstract</p> <p>Background</p> <p>The selection of relevant genes for sample classification is a common task in many gene expression studies. Although a number of tools have been developed to identify optimal gene expression signatures, they often generate gene lists that are too long to be exploited clinically. Consequently, researchers in the field try to identify the smallest set of genes that provide good sample classification. We investigated the genome-wide expression of the inflammatory phenotype in dendritic cells. Dendritic cells are a complex group of cells that play a critical role in vertebrate immunity. Therefore, the prediction of the inflammatory phenotype in these cells may help with the selection of immune-modulating compounds.</p> <p>Results</p> <p>A data mining protocol was applied to microarray data for murine cell lines treated with various inflammatory stimuli. The learning and validation data sets consisted of 155 and 49 samples, respectively. The data mining protocol reduced the number of probe sets from 5,802 to 10, then from 10 to 6 and finally from 6 to 3. The performances of a set of supervised classification models were compared. The best accuracy, when using the six following genes --Il12b, Cd40, Socs3, Irgm1, Plin2 and Lgals3bp-- was obtained by Tree Augmented Naïve Bayes and Nearest Neighbour (91.8%). Using the smallest set of three genes --Il12b, Cd40 and Socs3-- the performance remained satisfactory and the best accuracy was with Support Vector Machine (95.9%). These data mining models, using data for the genes Il12b, Cd40 and Socs3, were validated with a human data set consisting of 27 samples. Support Vector Machines (71.4%) and Nearest Neighbour (92.6%) gave the worst performances, but the remaining models correctly classified all the 27 samples.</p> <p>Conclusions</p> <p>The genes selected by the data mining protocol proposed were shown to be informative for discriminating between inflammatory and steady-state phenotypes in dendritic cells. The robustness of the data mining protocol was confirmed by the accuracy for a human data set, when using only the following three genes: Il12b, Cd40 and Socs3. In summary, we analysed the longitudinal pattern of expression in dendritic cells stimulated with activating agents with the aim of identifying signatures that would predict or explain the dentritic cell response to an inflammatory agent.</p

A power law global error model for the identification of differentially expressed genes in microarray data

BACKGROUND: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG. RESULTS: In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm. CONCLUSIONS: The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples

A novel checkpoint in the Bcl-2–regulated apoptotic pathway revealed by murine cytomegalovirus infection of dendritic cells

Infection with murine cytomegalovirus (MCMV) has contributed to understanding many aspects of human infection and, additionally, has provided important insight to understanding complex cellular responses. Dendritic cells (DCs) are a major target for MCMV infection. Here, we analyze the effects of MCMV infection on DC viability, and show that infected DCs become resistant to apoptosis induced by growth factor deprivation. The precise contribution of changes in the expression of Bcl-2 family proteins has been assessed and a new checkpoint in the apoptotic pathway identified. Despite their resistance to apoptosis, MCMV-infected DCs showed Bax to be tightly associated with mitochondria and, together with Bak, forming high molecular weight oligomers, changes normally associated with apoptotic cell death. Exposure of a constitutively occluded Bax NH2-terminal epitope was blocked after infection. These results suggest that MCMV has evolved a novel strategy for inhibiting apoptosis and provide evidence that apoptosis can be regulated after translocation, integration, and oligomerization of Bax at the mitochondrial membrane

amda 2 13 a major update for automated cross platform microarray data analysis

Microarray platforms require analytical pipelines with modules for data pre-processing including data normalization, statistical analysis for identification of differentially expressed genes, cluster analysis, and functional annotation. We previously developed the Automated Microarray Data Analysis (AMDA, version 2.3.5) pipeline to process Affymetrix 3′ IVT GeneChips. The availability of newer technologies that demand open-source tools for microarray data analysis has impelled us to develop an updated multi-platform version, AMDA 2.13. It includes additional quality control metrics, annotation-driven (annotation grade of Affymetrix NetAffx) and signal-driven (Inter-Quartile Range) gene filtering, and approaches to experimental design. To enhance understanding of biological data, differentially expressed genes have been mapped into KEGG pathways. Finally, a more stable and user-friendly interface was designed to integrate the requirements for different platforms. AMDA 2.13 allows the analysis of Affymetrix..