BRC77 Agilent Gene Expression Arrays
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Ellis MJ
PROVIDER: S-ECPF-GEOD-29442 | biostudies-other | 2012 Jun
REPOSITORIES: biostudies-other
Similar Datasets
Project description:BackgroundExpression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples.ResultsWe describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that interarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log(2) units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators.ConclusionsThis study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells.
| S-EPMC2909218 | biostudies-literature
Project description:BackgroundThe main research tool for identifying microRNAs involved in specific cellular processes is gene expression profiling using microarray technology. Agilent is one of the major producers of microRNA arrays, and microarray data are commonly analyzed by using R and the functions and packages collected in the Bioconductor project. However, an analytical package that integrates the specific characteristics of microRNA Agilent arrays has been lacking.ResultsThis report presents the new bioinformatic tool AgiMicroRNA for the pre-processing and differential expression analysis of Agilent microRNA array data. The software is implemented in the open-source statistical scripting language R and is integrated in the Bioconductor project (http://www.bioconductor.org) under the GPL license. For the pre-processing of the microRNA signal, AgiMicroRNA incorporates the robust multiarray average algorithm, a method that produces a summary measure of the microRNA expression using a linear model that takes into account the probe affinity effect. To obtain a normalized microRNA signal useful for the statistical analysis, AgiMicroRna offers the possibility of employing either the processed signal estimated by the robust multiarray average algorithm or the processed signal produced by the Agilent image analysis software. The AgiMicroRNA package also incorporates different graphical utilities to assess the quality of the data. AgiMicroRna uses the linear model features implemented in the limma package to assess the differential expression between different experimental conditions and provides links to the miRBase for those microRNAs that have been declared as significant in the statistical analysis.ConclusionsAgiMicroRna is a rational collection of Bioconductor functions that have been wrapped into specific functions in order to ease and systematize the pre-processing and statistical analysis of Agilent microRNA data. The development of this package contributes to the Bioconductor project filling the gap in microRNA array data analysis.
| S-EPMC3037903 | biostudies-literature
Project description:Agilent 44K whole genome studies were used to correlate gene expression values with mutations found in massively parallel sequencing experiments.
2012-06-21 | GSE29442 | GEO
Project description:Agilent 44K whole genome studies were used to correlate gene expression values with mutations found in massively parallel sequencing experiments. 77 patients were profiled using Agilent's two-color microarray platform. Tumor(cy5) was co-hybridized to a reference(cy3).
2012-06-20 | E-GEOD-29442 | biostudies-arrayexpress
Project description:Gene expression arrays are valuable and widely used tools for biomedical research. Today's commercial arrays attempt to measure the expression level of all of the genes in the genome. Effectively translating the results from the microarray into a biological interpretation requires an accurate mapping between the probesets on the array and the genes that they are targeting. Although major array manufacturers provide annotations of their gene expression arrays, the methods used by various manufacturers are different and the annotations are difficult to keep up to date in the rapidly changing world of biological sequence databases.We have created a consistent microarray annotation protocol applicable to all of the major array manufacturers. We constantly keep our annotations updated with the latest Ensembl Gene predictions, and thus cross-referenced with a large number of external biomedical sequence database identifiers. We show that these annotations are accurate and address in detail reasons for the minority of probesets that cannot be annotated. Annotations are publicly accessible through the Ensembl Genome Browser and programmatically through the Ensembl Application Programming Interface. They are also seamlessly integrated into the BioMart data-mining tool and the biomaRt package of BioConductor.Consistent, accurate and updated gene expression array annotations remain critical for biological research. Our annotations facilitate accurate biological interpretation of gene expression profiles.
| S-EPMC2894801 | biostudies-literature
Project description:BACKGROUND: Salmonella enterica serovar Enteritidis (SE) is one of the most common food-borne pathogens that cause human salmonellosis and usually results from the consumption of contaminated poultry products. The mechanism of SE resistance in chickens remains largely unknown. Previously, heterophils isolated from broilers with different genetic backgrounds (SE-resistant [line A] and -susceptible [line B]) have been shown to be important in defending against SE infections. To dissect the interplay between heterophils and SE infection, we utilized large-scale gene expression profiling. RESULTS: The results showed more differentially expressed genes were found between different lines than between infection (SE-treated) and non-infection (control) samples within line. However, the numbers of expressed immune-related genes between these two comparisons were dramatically different. More genes related to immune function were down-regulated in line B than line A. The analysis of the immune-related genes indicated that SE infection induced a stronger, up-regulated gene expression of line heterophils A than line B, and these genes include several components in the Toll-like receptor (TLR) signaling pathway, and genes involved in T-helper cell activation. CONCLUSION: We found: (1) A divergent expression pattern of immune-related genes between lines of different genetic backgrounds. The higher expression of immune-related genes might be more beneficial to enhance host immunity in the resistant line; (2) a similar TLR regulatory network might exist in both lines, where a possible MyD88-independent pathway may participate in the regulation of host innate immunity; (3) the genes exclusively differentially expressed in line A or line B with SE infection provided strong candidates for further investigating SE resistance and susceptibility. These findings have laid the foundation for future studies of TLR pathway regulation and cellular modulation of SE infection in chickens.
| S-EPMC2588606 | biostudies-literature
Project description:MicroRNA profiling of 3 Human NSCLC cell lines (PC9,PC9R,H1975)
| S-ECPF-MTAB-1327 | biostudies-other
Project description:BackgroundAffymetrix GeneChips are an important tool in many facets of biological research. Recently, notable design changes to the chips have been made. In this study, we use publicly available data from Affymetrix to gauge the performance of three human gene expression arrays: Human Genome U133 Plus 2.0 (U133), Human Exon 1.0 ST (HuEx) and Human Gene 1.0 ST (HuGene).ResultsWe studied probe-, exon- and gene-level reproducibility of technical and biological replicates from each of the 3 platforms. The U133 array has larger feature sizes so it is no surprise that probe-level variances are smaller, however the larger number of probes per gene on the HuGene array seems to produce gene-level summaries that have similar variances. The gene-level summaries of the HuEx array are less reproducible than the other two, despite having the largest average number of probes per gene. Greater than 80% of the content on the HuEx arrays is expressed at or near background. Biological variation seems to have a smaller effect on U133 data. Comparing the overlap of differentially expressed genes, we see a high overall concordance among all 3 platforms, with HuEx and HuGene having greater overlap, as expected given their design. We performed an analysis of detection rates and area under ROC curves using an experiment made up of several mixtures of 2 human tissues. Though it appears that the HuEx array has worse performance in terms of detection rates, all arrays have similar ability to separate differentially expressed and non-differentially expressed genes.ConclusionDespite noticeable differences in the probe-level reproducibility, gene-level reproducibility and differential expression detection are quite similar across the three platforms. The HuEx array, an all-encompassing array, has the flexibility of measuring all known or predicted exonic content. However, the HuEx array induces poorer reproducibility for genes with fewer exons. The HuGene measures just the well-annotated genome content and appears to perform well. The U133 array, though not able to measure across the full length of a transcript, appears to perform as well as the newer designs on the set of genes common to all 3 platforms.
| S-EPMC2216046 | biostudies-literature
Project description:We have developed a strategy for estimating gene expression on Affymetrix Exon arrays. The method includes a probe-specific background correction and a probe selection strategy in which a subset of probes with highly correlated intensities across multiple samples are chosen to summarize gene expression. Our results demonstrate that the proposed background model offers improvements over the default Affymetrix background correction and that Exon arrays may provide more accurate measurements of gene expression than traditional 3' arrays.
| S-EPMC1929160 | biostudies-literature
Project description:Genome-scale measurements of DNA methylation levels are necessary to decipher the epigenetic events involved in glioblastoma aggressive phenotype, and to guide new therapeutic strategies. In that purpose, we performed a whole genome integrative analysis of the methylation and expression profiles for 40 newly diagnosed glioblastoma patients. We have also screened for associations between CpG sites methylation levels and overall survival in a cohort of 50 patients uniformly treated with radiotherapy and chemotherapy with concomitant and adjuvant temozolomide (STUPP protocol). The methylation analysis identified 616 CpG sites differentially methylated between glioblastoma and control brain, a quarter being differentially expressed in a concordant way. Among these concordant CpG sites, 13 genes displayed, within our glioblastoma cohort, an inverse correlation between promoter methylation and expression levels: B3GNT5, FABP7, ZNF217, BST2, OAS1, SLC13A5, GSTM5, ME1, UBXD3, TSPYL5, FAAH, C7orf13, and C3orf14. The expression of these genes may be tightly regulated by epigenetic mechanisms. The survival analysis identified six CpG sites associated with overall survival. The SOX10 promoter methylation status (two CpG sites) stratifies the patients in a way similar to MGMT with improved performance based on Area Under the Curve criteria (0.78 vs. 0.71, p-value < 5.10-4). The methylation status of FNDC3B, TBX3, DGKI, and FSD1 promoters identify patients with MGMT methylated tumors non-responding to STUPP treatment (p-value < 1.10-4). These markers have a potential impact on therapeutic decision. 40 glioblastoma samples and 6 control brain samples were analysed. 2 distinct series of hybridizations were carried out, each containing GBMs and control brains.
| S-ECPF-GEOD-22866 | biostudies-other