Ontology highlight
ABSTRACT:
PROVIDER: PRJNA134153 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Forty-four paired (from the same patient) samples of head and neck squamous cell carcinoma (HNSCC) and normal tissue were studied with Affymetrix U95A chips. A stringent multi-test approach, combining 7 traditional and microarray-specific statistical tests, was used to analyze the resultant data. Candidate genes were assigned to tiers of significance based on the number of statistical tests that each gene satisfied. Representative genes (both up-regulated and down-regulated) from each of the 3 tiers would be quantified with RT-PCR on both microarray-tested and new samples of HNSCC. The goal of this study is to identify reliable differentially-expressed genes on HNSCC and to testify our hypothesis whether or not a combinatorial approach (multi-tests) to analyzing microarray data can really identify differentially-expressed genes with fewer false-positives. Keywords: disease state; tumor vs. normal
2007-01-20 | GSE6631 | GEO
Project description:High-throughput DNA methylation profiling exploits microarray technologies thus providing a wealth of data, which however solicits rigorous, generic and analytical pipelines for an efficient systems level analysis and interpretation. In the present study, we utilize the Illumina's Infinium Human Methylation 450K BeadChip platform in an epidemiological cohort, targeting to associate interesting methylation patterns with breast cancer predisposition. The computational framework proposed here extends the -established in transcriptomic microarrays- logarithmic ratio of the Methylated versus the Unmethylated signal intensities, quoted as M-value. Moreover, intensity-based correction of the M-signal distribution is introduced in order to correct for batch effects and probe-specific errors in intensity measurements. This is accomplished through the estimation of intensity-related error measures from quality control samples included in each chip. Moreover, robust statistical measures based on coefficient variation measurements of DNA methylation between control and case samples alleviate the impact of technical variation. The results presented here are juxtaposed to those derived by applying classical pre-processing and statistical selection methodologies. Overall, in comparison to traditional approaches, the introduced framework's superior performance in terms of technical bias correction, along with its generic character, support its suitability for various microarray technologies.
2016-01-01 | GSE52635 | GEO
Project description:To confirm the suitability of C. elegans as a model organism, preliminary gene expression experiments were performed using juglone (5-hydroxy-1,4-naphthoquinone; 97% pure; Sigma-Aldrich, St-Louis, MO), an ROS-generator, as a positive control of oxidative stress (d'Arcy Doherty et al. 1987; Leiers et al. 2003). Reproduction tests were initially performed to establish the sub-lethal effect concentrations used in the microarray experiments.
2014-07-17 | GSE59475 | GEO
Project description:High-throughput DNA methylation profiling exploits microarray technologies thus providing a wealth of data, which however solicits rigorous, generic and analytical pipelines for an efficient systems level analysis and interpretation. In the present study, we utilize the Illumina's Infinium Human Methylation 450K BeadChip platform in an epidemiological cohort, targeting to associate interesting methylation patterns with breast cancer predisposition. The computational framework proposed here extends the -established in transcriptomic microarrays- logarithmic ratio of the Methylated versus the Unmethylated signal intensities, quoted as M-value. Moreover, intensity-based correction of the M-signal distribution is introduced in order to correct for batch effects and probe-specific errors in intensity measurements. This is accomplished through the estimation of intensity-related error measures from quality control samples included in each chip. Moreover, robust statistical measures based on coefficient variation measurements of DNA methylation between control and case samples alleviate the impact of technical variation. The results presented here are juxtaposed to those derived by applying classical pre-processing and statistical selection methodologies. Overall, in comparison to traditional approaches, the introduced framework's superior performance in terms of technical bias correction, along with its generic character, support its suitability for various microarray technologies. Bisulphite converted DNA from the samples were hybridized to the Illumina Infinium HumanMethylation450 BeadChip
2016-07-03 | E-GEOD-52635 | biostudies-arrayexpress
Project description:Microarray technology has enabled the measurement of comprehensive transcriptomic information. However, each data entry may reflect trivial individual differences among samples and also contain technical noise. Therefore, the certainty of each observed difference should be confirmed at earlier steps of the analyses, and statistical tests are frequently used for this purpose. Since a microarray measures a huge number of genes and the results are processed simultaneously, concerns regarding problems of multiplicity have been raised to the tests. To deal with these problems, several methodologies have been proposed, making the tests very conservative. Indeed, arbitrary tuning of the test threshold has also been introduced to relax the test conditions. However, the appropriateness of the multiplicity problems as well as the compensation methods has not been confirmed. The appropriateness of the compensation methods was checked by means of coincidence of the premises of the methodologies with the observed characteristics found in real data of two typical platforms of microarray analysis. Normality was observed in within-group data variations, supporting applications of parametric tests. However, genes displayed their own tendencies in the magnitude of variations, and the distributions of P-values were rather complex and varied; these characteristics are inconsistent with the premises of the compensation methodologies. Additionally, the appropriateness of the proposed multiplicities is reconsidered. When we observed differences in the transcriptome, the family-wise error rate should not be considered, since analyses at higher levels would not be influenced by a few false positives among the huge numbers of true information. Likely, concerns for a false discovery rate are not suitable for the point null hypotheses on expression levels, since the rate of true null hypotheses should be rare in contradiction to the premise of the methodology. Although compensation methods have been recommended to deal with the problem of multiplicity, the compensations are actually inappropriate for many of the applications of transcriptome analyses. Compensations are not only unnecessary, but will increase the occurrence of false negative errors, and arbitrary adjustment of the threshold damages the objectivity of the tests. Rather, the results of parametric tests should be evaluated directly. This SuperSeries is composed of the SubSeries listed below.
2010-11-17 | GSE25410 | GEO
Project description:Forty-four paired (from the same patient) samples of head and neck squamous cell carcinoma (HNSCC) and normal tissue were studied with Affymetrix U95A chips. A stringent multi-test approach, combining 7 traditional and microarray-specific statistical tests, was used to analyze the resultant data. Candidate genes were assigned to tiers of significance based on the number of statistical tests that each gene satisfied. Representative genes (both up-regulated and down-regulated) from each of the 3 tiers would be quantified with RT-PCR on both microarray-tested and new samples of HNSCC. The goal of this study is to identify reliable differentially-expressed genes on HNSCC and to testify our hypothesis whether or not a combinatorial approach (multi-tests) to analyzing microarray data can really identify differentially-expressed genes with fewer false-positives. Experiment Overall Design: Paired HNSCC tumor and normal samples from 22 patients were evaluated for differential gene expression on Affymetrix U95A chips.
2009-02-06 | E-GEOD-6631 | biostudies-arrayexpress
Project description:MicroRNA miRNA expression profiles for human ovarian carcinomas were examined to investigate the miRNA involvement in the development of this neoplasia. miRNA microarray analysis identified statistical unique profiles, which could discriminate ovarian carcinomas from noncancerous ovarian tissues, and different groups of tumors classified according to histo-pathological characteristics.
2007-10-31 | E-TABM-343 | biostudies-arrayexpress
Project description:Human pluripotent stem cell (hPSC) lines exhibit repeated patterns of genetic variation, which can alter in vitro properties as well as suitability for clinical use. We examined associations between copy number variations (CNVs) on chromosome 17 and hPSC mesodiencephalic dopaminergic (mDA) differentiation. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from cryopreserved stem cell samples. Copy number analysis of Affymetrix Genome-Wide Human SNP Array 6.0 was performed for 17 stem cell samples.
2014-12-17 | E-GEOD-64241 | biostudies-arrayexpress
Project description:Human pluripotent stem cell (hPSC) lines exhibit repeated patterns of genetic variation, which can alter in vitro properties as well as suitability for clinical use. We examined associations between copy number variations (CNVs) on chromosome 17 and hPSC mesodiencephalic dopaminergic (mDA) differentiation.
2014-12-17 | GSE64241 | GEO