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cDNA microarrays

cDNA microarrays. cDNA microarrays. small slides with several measurement units, spots e.g. 2.5cm-by-7.6cm glass slide with 30,000 spots each spot contains specific nucleotide sequences, probes in hybridization process, labeled (Cy5, Cy3) samples attach to probes

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cDNA microarrays

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  1. cDNA microarrays Panu Somervuo, March 19, 2007

  2. cDNA microarrays • small slides with several measurement units, spots • e.g. 2.5cm-by-7.6cm glass slide with 30,000 spots • each spot contains specific nucleotide sequences, probes • in hybridization process, labeled (Cy5, Cy3) samples attach to probes • comparative genome hybridization (CGH): DNA samples • gene expression: RNA samples • relative intensity of hybridization can be measured Cy5 Cy3 Panu Somervuo, March 19, 2007

  3. Data flow • biological data, DNA/RNA extraction, fluoresence dye labeling, hybridizationarray • scanningimage • image processing: spot segmentationdatafile • data preprocessing and normalization: • data analysis1: statistical tests to find differentially expressed genes  gene lists • data analysis2: biological interpretations of results Panu Somervuo, March 19, 2007

  4. Image processing • segmentation: spot signals are extracted from background • intensity information from both spot foreground and background • other information like spot size and shape Panu Somervuo, March 19, 2007

  5. Image analysis results file Panu Somervuo, March 19, 2007

  6. Plotting data Panu Somervuo, March 19, 2007

  7. Logarithm of ratio • log(Cy5/Cy3) = log(Cy5) – log(Cy3) • log2(4/1) = 2 • log2(2/1) = 1 • log2(1/1) = 0 • log2(1/2) = -1 • log2(1/4) = -2 Panu Somervuo, March 19, 2007

  8. Plotting data • scatterplot • MA plot (Ratio vs Intensity) Panu Somervuo, March 19, 2007

  9. Panu Somervuo, March 19, 2007

  10. Normalization • goal: to remove the effects of non-biological causes from data (dye-effect, hybridization, scanning, noise) and keep the biological information as well as possible • normalization can be based on the behavior of the majority of the spots on the array, or small set of special control spots • each normalization method is based on some assumption of the data Panu Somervuo, March 19, 2007

  11. Spot background subtraction • how to know if spot signal is real and not just noise? • comparison against background signal • global versus local background • should background subtraction be used or not? Panu Somervuo, March 19, 2007

  12. Normalization • can be applied to both single channel and ratio data • mean • variance Panu Somervuo, March 19, 2007

  13. Mean normalization • global mean vs intensity dependent mean • Loess/Lowess normalization Panu Somervuo, March 19, 2007

  14. Print tip loess normalization Panu Somervuo, March 19, 2007

  15. Panu Somervuo, March 19, 2007

  16. Control spots (spike-in controls) fold change up 10 log2(10)=3.32 fold change up 3 log2(3)=1.58 fold change down 3 log2(1/3)=-1.58 fold change down 10 log2(1/10)=-3.32 Panu Somervuo, March 19, 2007

  17. What is the best normalization method? • each method is based on some assumption  each method can fail • if utilizing the behavior of majority of the spots, array should represent all genes • if utilizing control spots, check if they are reliable • lots of methods have been introduced, lots of methods will be introduced… Panu Somervuo, March 19, 2007

  18. Finding differentially expressed genes • Manually set fold change cutoff • Fold change cutoff based on data • Statistical test, p-value Panu Somervuo, March 19, 2007

  19. Limma package in R • analysis of microarray data • data import • data plotting • data normalization • statistical tests  differentially expressed genes • online help and tutorial available > help(package=limma) > library(limma) > limmaUsersGuide() Panu Somervuo, March 19, 2007

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