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Enhancing Molecular Detection with ViaLogy: Insights from Summer Project Analysis

This project by Ronald J. Perez at ViaLogy focused on improving molecular detection through advanced computational products like ViaAmp and Gene expression amplification software. Key findings included a comparison of signal processing technologies highlighting the limitations of passive analysis. The analysis involved processing 18 microarray images, classifying genes based on signal-to-noise ratios, and evaluating the reproducibility of GenePix outputs. Future directions include further analysis with ViaAmp to enhance sensitivity and specificity in molecular detection systems.

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Enhancing Molecular Detection with ViaLogy: Insights from Summer Project Analysis

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  1. Summer at ViaLogy Ronald J. Perez

  2. ViaLogy Developers of computational products for increased performance of molecular detection systems • ViaAmp • Gene expression amplification software designed to use active signal processing technology • Differentiation of true signal from background noise

  3. Project • Showing the limitations of passive analysis • Standard microarray image analysis software represented by GenePix • Deliverables • Processed 18 microarray images using passive analysis • Classified arrays into triplicates according to dilution • Given initial condition: ratio of green to red intensity is 1 • Focus on array intensities as opposed to gene regulation

  4. What did I analyze? • 18 Microarrays, 340 spots on each array and since each gene is in duplicate, there are a total of 170 genes • There were 6 different levels of dilution across 18 arrays

  5. Most Concentrated Array Most Diluted Array

  6. GenePix Output File

  7. Calculating Signal to Noise • There are two ways to calculate signal to noise ratio (S/N) from a microarray spot: • The first S/N definition used by Vialogy is calculated the following way: • S/N (1) = Foreground Median – Background • The second S/N definition used by the client who sent us the arrays is: • S/N (2) = (Foreground Mean – Background)/SD of Background

  8. GenePix Reproducibility • If GenePix data were 100% reproducible, one would see a line with slope of 1 when plotting the S/N ratios of two independent analysis. • When a scatter plot was made, some data points did not fall on a straight line. • Since most data points fall on a straight line, we assumed the output data is credible and safe to continue analyzing.

  9. Reproducibility Graph

  10. Microarray Categorization • The first approach taken to classify this set of 18 arrays into triplets was to plot the S/N ratio of all 170 genes vs. arrays. • This plot will give a rough idea of the intensity pattern of these microarrays. • Did GenePix do a good job analyzing these microarrays?

  11. Gene Intensity per Array

  12. Client Selected Groups • Instead of looking at all 170 genes, our client gave us a list of 48 genes to focus on. • These genes had a S/N ratio greater than 2 and where classified into the following 4 groups: • Focus on C and D because they have highest S/N ratio

  13. Analysis of Groups C and D • Genes in Levels C and D were used to design a different categorization scheme. • S/N ratios of Genes in Level C were summed up and the MEAN was taken separately for each array. Same was done for Level D genes. • Level C and D MEANS were averaged. • S/N ratios of Genes in Level C were summed up and this time the MEDIAN was taken separately for each array. Same was done for Level D genes • Level C and D MEDIANS were also averaged.

  14. Mean and Median Approach MEAN INTENSITY MEDIAN INTENSITY

  15. Categorization Summary

  16. Future Direction • I have only told half of the story, the next steps are to: • Process microarrays using ViaAmp • Passive analysis vs. active analysis • ViaAmp results • Sensitivity and Specificity studies

  17. Thanks • Vialogy Team - Dr. David Robbins • SoCalBSI Team • NSF, NIH

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