A comprehensive analysis of the effect of microarray data

Background: Microarray data preprocessing, such as differentially expressed (DE) genes selection, is performed prior to higher level statistical analysis in order to account for technical variability. Preprocessing for the Affymetrix GeneChip includes background correction, normalisation and summarisation. Numerous preprocessing methods have been proposed with little consensus as to which is the most suitable. Furthermore, due to poor concordance among results from cross-platform analyses, protocols are being developed to enable cross-platform reproducibility. However, the effect of data analysis on a single platform is still unknown. The objective of our study is two-fold: first to determine whether there is consistency in the results obtained from a single platform; and second to investigate the effect of preprocessing on DE genes selection, analysed on four datasets. Results: Results indicate that microarray analysis is subjective. The lists of DE genes are variable and dependent on the preprocessing method used. Furthermore, the characteristics of the dataset, and the type of DE genes identification method used, greatly affect the outcome. Despite using a single platform, there is a lot of variability in the results. Conclusions: This is the first comprehensive analysis using multiple datasets generated from a single platform and involving many DE genes selection methods to assess the effect of data preprocessing on downstream analysis. Results indicate that preprocessing methods affect downstream analysis. Results are also affected by the kind of data and statistical analysis tools used. Our study reveals that there are inconsistencies in results obtained from a single platform. These issues have been overlooked in past reports

A semi-parametric Bayesian model for unsupervised differential co-expression analysis

<p>Abstract</p> <p>Background</p> <p>Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples.</p> <p>Results</p> <p>We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ER<it>α </it>regulatory network.</p> <p>Conclusions</p> <p>We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.</p

The future of hybrid imaging—part 3: PET/MR, small-animal imaging and beyond

Since the 1990s, hybrid imaging by means of software and hardware image fusion alike allows the intrinsic combination of functional and anatomical image information. This review summarises in three parts the state of the art of dual-technique imaging with a focus on clinical applications. We will attempt to highlight selected areas of potential improvement of combined imaging technologies and new applications. In this third part, we discuss briefly the origins of combined positron emission tomography (PET)/magnetic resonance imaging (MRI). Unlike PET/computed tomography (CT), PET/MRI started out from developments in small-animal imaging technology, and, therefore, we add a section on advances in dual- and multi-modality imaging technology for small animals. Finally, we highlight a number of important aspects beyond technology that should be addressed for a sustained future of hybrid imaging. In short, we predict that, within 10 years, we may see all existing multi-modality imaging systems in clinical routine, including PET/MRI. Despite the current lack of clinical evidence, integrated PET/MRI may become particularly important and clinically useful in improved therapy planning for neurodegenerative diseases and subsequent response assessment, as well as in complementary loco-regional oncology imaging. Although desirable, other combinations of imaging systems, such as single-photon emission computed tomography (SPECT)/MRI may be anticipated, but will first need to go through the process of viable clinical prototyping. In the interim, a combination of PET and ultrasound may become available. As exciting as these new possible triple-technique—imaging systems sound, we need to be aware that they have to be technologically feasible, applicable in clinical routine and cost-effective

Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms

The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms

WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results

Cluster analysis methods have been extensively researched, but the adoption of new methods is often hindered by technical barriers in their implementation and use. WebGimm is a free cluster analysis web-service, and an open source general purpose clustering web-server infrastructure designed to facilitate easy deployment of integrated cluster analysis servers based on clustering and functional annotation algorithms implemented in R. Integrated functional analyses and interactive browsing of both, clustering structure and functional annotations provides a complete analytical environment for cluster analysis and interpretation of results. The Java Web Start client-based interface is modeled after the familiar cluster/treeview packages making its use intuitive to a wide array of biomedical researchers. For biomedical researchers, WebGimm provides an avenue to access state of the art clustering procedures. For Bioinformatics methods developers, WebGimm offers a convenient avenue to deploy their newly developed clustering methods. WebGimm server, software and manuals can be freely accessed at http://ClusterAnalysis.org/

Expression profiling of human donor lungs to understand primary graft dysfunction after lung transplantation

Lung transplantation is the treatment of choice for end-stage pulmonary diseases. A limited donor supply has resulted in 4000 patients on the waiting list. Currently, 10-20% of donor organs offered for transplantation are deemed suitable under the selection criteria, of which 15-25% fails due to primary graft dysfunction (PGD). This has resulted in increased efforts to search for alternative donor lungs selection criteria. In this study, we attempt to further our understanding of PGD by observing the changes in gene expression across donor lungs that developed PGD versus those that did not. Our second goal is to use a machine learning tool - support vector machine (SVM), to distinguish unsuitable donor lungs from suitable donor lungs, based on the gene expression data. From our analysis, we have obtained transcripts that were involved in signalling, apoptosis and stress-activated pathways. Results also indicate that metallothionein 3 may prevent lungs from developing PGD. Preliminary classification results for distinguishing suitable and unsuitable lungs for transplantation using a SVM were promising. This is the first such attempt to use human lungs used for transplantation and combine the identification of a molecular signature for PGD, with machine learning methods for donor lung prediction

Genomics Portals: integrative web-platform for mining genomics data

<p>Abstract</p> <p>Background</p> <p>A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems.</p> <p>Results</p> <p>Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis.</p> <p>Conclusion</p> <p>The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at <url>http://GenomicsPortals.org</url>.</p

TNFR2 maintains adequate IL-12 production by dendritic cells in inflammatory responses by regulating endogenous TNF levels

Sepsis-induced immune reactions are reduced in TNF receptor 2 (TNFR2)-deficient mice as previously shown. In order to elucidate the underlying mechanisms, the functional integrity of myeloid cells of TNFR2-deficient mice was analyzed and compared to wild type (WT) mice. The capacity of dendritic cells to produce IL-12 was strongly impaired in TNF-deficient mice, mirroring impaired production of IL-12 by WT dendritic cells in sepsis or after LPS or TNF pre-treatment. In addition, TNFR2-deficient mice were refractory to LPS pre-treatment and also to hyper-sensitization by inactivated Propionibacterium acnes, indicating habituation to inflammatory stimuli by the immune response when TNFR2 is lacking. Constitutive expression of TNF mRNA in kidney, liver, spleen, colon and lung tissue, and the presence of soluble TNFR2 in urine of healthy WT mice supported the conclusion that TNF is continuously present in naïve mice and controlled by soluble TNFR2. In TNFR2-deficient mice endogenous TNF levels cannot be balanced and the continuous exposure to enhanced TNF levels impairs dendritic cell function. In conclusion, TNF pre-exposure suppresses secondary inflammatory reactions of myeloid cells; therefore, continuous control of endogenous TNF by soluble TNFR2 seems to be essential for the maintenance of adequate sensitivity to inflammatory stimuli