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Biomol GmbH
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MolGen LLC
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GoldenGate Software Inc
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MolGen LLC
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Image Search Results
Journal: BMC Bioinformatics
Article Title: SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data
doi: 10.1186/1471-2105-7-205
Figure Lengend Snippet: Comparison of the properties of different DNA-microarray simulation models described in literature. '+' and '-' indicate availability of the indicated feature in the specified model. Note that the modeling of features in the specific models is usually not the same.
Article Snippet: To illustrate the use of SIMAGE in drawing meaningful conclusions about the design and analysis of DNA microarray experiments we show a number of examples based on
Techniques: Comparison, Gene Expression, Software
Journal: BMC Bioinformatics
Article Title: SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data
doi: 10.1186/1471-2105-7-205
Figure Lengend Snippet: Distribution of the deviations of several of the model parameters estimated from 100 simulated DNA-microarray slides. The deviation is calculated as (estimate - true value) / (standard deviation of 100 estimates).
Article Snippet: To illustrate the use of SIMAGE in drawing meaningful conclusions about the design and analysis of DNA microarray experiments we show a number of examples based on
Techniques: Microarray, Standard Deviation
Journal: BMC Bioinformatics
Article Title: SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data
doi: 10.1186/1471-2105-7-205
Figure Lengend Snippet: Distribution of p -values of a DNA-microarray experiment simulated by SIMAGE . Data for 2200 genes, in 6 slides with technical duplicates hybridized in dye-swaps, was simulated using the MolGen experiment profile (supplementary Table T1) with some changes: π - = 1% and π + = 2%, μ - = -2 and μ + = 2), σ bg = 700, and s = 30 % × μ . The main graph shows the resulting ratios after normalization plotted versus the p -value. The graph was simplified by removing genes with ratios between 2/3 and 3/2. The 66 genes for which differential expressions were modeled are depicted by blue diamonds. The remaining genes are depicted in purple squares. The small graph on the right demonstrates the reversed p -value dependency on the average signal for the 66 differentially expressed genes modeled. The average signal was calculated for each of the 66 genes over the maximum of 12 normalized measurements. Normalization was performed using Lowess normalization and differential expression tests were performed with the non-Bayesian Cyber-T implementation of a variant of the t -test [3]. The Cyber-T test provides the p -values, which indicate the probability that a given ratio is not differential caused by chance. Genes with less than 8 measurements were excluded from these tests and assigned a p -value of 1, in order to be able to present these genes in the graph.
Article Snippet: To illustrate the use of SIMAGE in drawing meaningful conclusions about the design and analysis of DNA microarray experiments we show a number of examples based on
Techniques: Microarray, Quantitative Proteomics, Variant Assay
Journal: BMC Bioinformatics
Article Title: SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data
doi: 10.1186/1471-2105-7-205
Figure Lengend Snippet: Estimation of parameters from the simulation of 100 DNA-microarray slides. The mentioned deviations are the number of estimated standard-deviations that the estimated mean, respectively median, lie away from the true value of the parameter.
Article Snippet: To illustrate the use of SIMAGE in drawing meaningful conclusions about the design and analysis of DNA microarray experiments we show a number of examples based on
Techniques:
Journal: Genes
Article Title: Epigenetic Heterogeneity of B-Cell Lymphoma: DNA Methylation, Gene Expression and Chromatin States
doi: 10.3390/genes6030812
Figure Lengend Snippet: DNA methylation summary characteristics of lymphoma and of healthy B and GCB cells. ( a ) The frequency distribution of the promoter methylation beta values of B-cells shows two maxima referring to almost not- and completely methylated promoters, respectively; The distributions of beta values loose this bimodality to a large degree in lymphoma where weakly and intermediately methylated genes become hyper-methylated and highly methylated genes become hypo-methylated compared with healthy B-cells ( b + c ); ( d ) The total methylation level increases and ( e ) the variability of methylation among the genes in each of the samples decreases.
Article Snippet: Microarray-derived
Techniques: DNA Methylation Assay, Methylation
Journal: Genes
Article Title: Epigenetic Heterogeneity of B-Cell Lymphoma: DNA Methylation, Gene Expression and Chromatin States
doi: 10.3390/genes6030812
Figure Lengend Snippet: SOM portraying of the DNA methylation landscape of lymphoma (MetSOM). ( a ) SOM portraits of histological lymphoma classes and of controls. Red and blue colors assign regions containing genes with high and low methylation levels, respectively; ( b ) the methylation overview map summarizes regions hypermethylated in any of the classes compared with any other one in red. The methylation variance map identifies regions of highly variable (red) and almost invariant (blue) beta values; ( c ) The methylation profiles show the mean methylation level among the samples of genes taken from the “spot” regions 1–6 assigned in the methylation overview map. Horizontal dashed lines serve as guide for the eye showing the mean methylation level of the respective spot averaged over all samples. Assignments as “hyper-” or “hypomethylated” refer to relative methylations compared with B-cells. Lists of genes in these regions are given in .
Article Snippet: Microarray-derived
Techniques: DNA Methylation Assay, Methylation