Similar Datasets
Project description:Study for evaluating the effect of different microarray platforms on gene expression measurements
| PRJNA531720 | ENA
Project description:Comparison of concordance in single and multi-gene genomic indices from data generated by two different laboratories (MD Anderson Cancer Center (MDA) and Jules Bordet Institute (JBI)) and on two different Affymetrix platforms (U113A and U133_Plus2). We used a 2x2 factorial study in which 16 clinical breast cancer samples were profiled by both laboratories on both platforms (64 arrays total). Total RNA was extracted, cRNA was labeled and fragmented at MDA. Fragmented cRNA was profiled on both platforms at MDA. Fragmented cRNA was shipped to JBI where it was also profiled on both platforms.
2010-08-13 | E-GEOD-17700 | biostudies-arrayexpress
Project description:Multiple commercial microarrays for measuring genome-wide gene expression levels are currently available, including oligonucleotide and cDNA, single- and two-channel formats. This study reports on the results of gene expression measurements generated from identical RNA preparations that were obtained using three commercially available microarray platforms. RNA was collected from PANC-1 cells grown in serum-rich medium and at 24 h following the removal of serum. Three biological replicates were prepared for each condition, and three experimental replicates were produced for the first biological replicate. RNA was labeled and hybridized to microarrays from three major suppliers according to manufacturers' protocols, and gene expression measurements were obtained using each platform's standard software. For each platform, gene targets from a subset of 2009 common genes were compared. Correlations in gene expression levels and comparisons for significant gene expression changes in this subset were calculated, and showed considerable divergence across the different platforms, suggesting the need for establishing industrial manufacturing standards, and further independent and thorough validation of the technology.
| S-EPMC206463 | biostudies-literature
Project description:Comparison of concordance in single and multi-gene genomic indices from data generated by two different laboratories (MD Anderson Cancer Center (MDA) and Jules Bordet Institute (JBI)) and on two different Affymetrix platforms (U113A and U133_Plus2). We used a 2x2 factorial study in which 16 clinical breast cancer samples were profiled by both laboratories on both platforms (64 arrays total).
2010-08-13 | GSE17700 | GEO
Project description:A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.
| S-EPMC2570215 | biostudies-literature
Project description:BackgroundPresently, multiple options exist for conducting gene expression profiling studies in swine. In order to determine the performance of some of the existing microarrays, Affymetrix Porcine, Affymetrix Human U133+2.0, and the U.S. Pig Genome Coordination Program spotted glass oligonucleotide microarrays were compared for their reproducibility, coverage, platform independent and dependent sensitivity using fibroblast cell lines derived from control and parthenogenic porcine embryos.ResultsArray group correlations between technical replicates demonstrated comparable reproducibility in both Affymetrix arrays. Glass oligonucleotide arrays showed greater variability and, in addition, approximately 10% of probes had to be discarded due to slide printing defects. Probe level analysis of Affymetrix Human arrays revealed significant variability within probe sets due to the effects of cross-species hybridization. Affymetrix Porcine arrays identified the greatest number of differentially expressed genes amongst probes common to all arrays, a measure of platform sensitivity. Affymetrix Porcine arrays also identified the greatest number of differentially expressed known imprinted genes using all probes on each array, an ad hoc measure of realistic performance for this particular experiment.ConclusionWe conclude that of the platforms currently available and tested, the Affymetrix Porcine array is the most sensitive and reproducible microarray for swine genomic studies.
| S-EPMC1769376 | biostudies-literature
Project description:Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.
| S-EPMC2920073 | biostudies-literature
Project description:BACKGROUND: Microarrays have become a routine tool to address diverse biological questions. Therefore, different types and generations of microarrays have been produced by several manufacturers over time. Likewise, the diversity of raw data deposited in public databases such as NCBI GEO or EBI ArrayExpress has grown enormously.This has resulted in databases currently containing several hundred thousand microarray samples clustered by different species, manufacturers and chip generations. While one of the original goals of these databases was to make the data available to other researchers for independent analysis and, where appropriate, integration with their own data, current software implementations could not provide that feature.Only those data sets generated on the same chip platform can be readily combined and even here there are batch effects to be taken care of. A straightforward approach to deal with multiple chip types and batch effects has been missing.The software presented here was designed to solve both of these problems in a convenient and user friendly way. RESULTS: The virtualArray software package can combine raw data sets using almost any chip types based on current annotations from NCBI GEO or Bioconductor. After establishing congruent annotations for the raw data, virtualArray can then directly employ one of seven implemented methods to adjust for batch effects in the data resulting from differences between the chip types used. Both steps can be tuned to the preferences of the user. When the run is finished, the whole dataset is presented as a conventional Bioconductor "ExpressionSet" object, which can be used as input to other Bioconductor packages. CONCLUSIONS: Using this software package, researchers can easily integrate their own microarray data with data from public repositories or other sources that are based on different microarray chip types. Using the default approach a robust and up-to-date batch effect correction technique is applied to the data.
| S-EPMC3599117 | biostudies-literature
Project description:BACKGROUND: The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. RESULTS: Gene expression profiles in the hippocampus of five wild-type and five transgenic deltaC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms. CONCLUSION: The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression.
| S-EPMC2335120 | biostudies-literature
Project description:To evaluate the effect of intratumorally heterogeneity on gene expression measurements, we profiled and compared samples from different macroscopic tumor areas. Information was used to select probe sets with a high spatial reproducibility for the development of gene signatures.
2019-05-30 | GSE129557 | GEO