Journal of Biomedical Discovery and Collaboration
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Organizational Co-Authorships from First 130 DNA Microarray Articles.
- For example, in Figure 4, Affymetrix (large green node) co-authored with Princeton, but Princeton did not co-author with NIH (large blue node), so Princeton is near Affymetrix but distant from NIH. Furthermore, although Affymetrix and NIH co-authored papers together, they also co-authored papers with several organizations that did not co-author with both organizations, thus Affymetrix and NIH are pulled some distance apart (as opposed to NIH and Stanford, the large red node, which share more institutional c
BMC Bioinformatics
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Schematic diagram depicting ArrayQuest system topography and steps in the process of performing an analysis of DNA microarray data. As indicated, DNA microarray data can be obtained from multiple sources including the MUSC DNA Microarray Database, the NIH GEO database or a user's private database.
- The ArrayQuest system allows Internet clients to connect to a center point web-server to create a new analysis project or select an existing project folder (Fig. 1).
- When an analysis script has completed execution, the files are copied from the analysis computer back to the center point web server for viewing (Fig. 1).
BMC Bioinformatics
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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).
- Deviations of the mean and median of the estimated parameters from the original parameters are shown in Table 3 and Figure 3.
- Parameter σpin is somewhat systematically overestimated (Fig. 3).
- Table 3 and Figure 3 show results for the choice of μ- = -1, μ+ = 1, π- = π+ = 10%, hence when the proportions of regulated genes and their average shifts in log ratio are considerable.
- In some cases where the true values of μ-, μ+, π-, and π+ are small their estimates are rather poor (Fig. 3).
BMC Bioinformatics
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Schematic overview of the SIMAGE model (A) and a few layers visualized (B and C). The blue-marked boxes (A) indicate layers that are further visualized (B and C). A simulation of the entire 'non-biological' signal is shown in B and C. Top row, sum of the gradient effects and density effects; second row, spot pin effects; third row: Gaussian noise. The bottom row shows the sum of all the effects pictured in the top three rows. The signals are plotted three-dimensionally (left side view) and two-dimensionally (right side view).
- Figure 1 shows a schematic and simplified overview of those levels in dual-color DNA microarray data (top) as well as the effect of some of these levels on the composition of the expression signals (bottom).
- It is obvious that knowledge about the properties that gene expression signals hold as shown in Figure 1 is very important to any researcher.
- And in case it is not (yet) obvious for a novice in the field, the visualization as shown in Figure 1 can be of educational value.
- Figure 1 shows a schematic representation of the model used in this study.
- These densities are summed, together with a linear gradient with a random tilt lower than s, to constitute the background surface signals (see Fig. 1B, top panel; see also the supplementary document).
- There is an important educational aspect about the simulation of DNA-microarray data, which is clearly illustrated in Figure 1B: it provides clear insights in the contribution of each background layer of the model to the measured signal.
BMC Bioinformatics
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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), σbg = 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.
- SIMAGE allows modeling differentially expressed genes as is demonstrated in an in silico simulated experiment (Fig. 6).
- In almost all cases the p-values and ratios of the 66 modeled differentially expressed genes (the blue diamonds in Fig. 6) are significantly lower than those of the non-modeled genes (the red squares in Fig. 6).
- The inset in Figure 6 clearly demonstrates the signal dependency of the p-values: differentially expressed genes with higher expression levels are assigned lower p-values.
Journal of Biomedical Discovery and Collaboration
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Federal Funding for R&D to California Industrial Firms and to Universities (in millions of dollars).
- Table 2 and Figure 2 illustrate the significant contribution of federal funding of both university and industry R&D in California for the period of the 1990s to 2002.
Journal of Biomedical Discovery and Collaboration
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Small Business Innovation Research and Technology Awards to Silicon Valley.
- Figure 3 provides an overview of SBIR and STTR awards specifically to the Bay Area.
Journal of Biomedical Discovery and Collaboration
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Number of Microarray Articles by Subject (1998–2004).
- Figure 5 presents the number of microarray articles by subject over a seven year period in order to demonstrate the growing relevance of microarrays to diverse disciplines.
- Following, Figure 6 attempts to capture a similar picture of the spread of microarrays into different corners of academia through the departmental affiliations of authors rather than the subject classification of the article as in Figure 5.
Journal of Biomedical Discovery and Collaboration
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Concept of Light-Directed Spatially Addressable Parallel Chemical Synthesis. Source: Fodor SPA, Stryer L, Read JL, Pirrung MC: USPTO 5,744,305. Arrays of Materials Attached to a Substrate, April 28, 1998, Sheet 1.
- What they demonstrated by 1991 in a now classic article published in Science was a process for depositing onto a glass substrate – literally a microscope slide cover in the first version of the invention – amino acid groups – NH – that were blocked by a photolabile protecting chemical group – X (Figure 1) [9].