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cDNA Microarrays

cDNA Microarrays

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cDNA Microarrays

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  1. cDNA Microarrays MB206

  2. What is a cDNA Microarray? • Also known as DNA Chip • Allows simultaneous measurement of the level of transcription for every gene in a genome (gene expression) • Transcription? • Process of copying of DNA into messenger RNA (mRNA) • Environment dependant! • Microarray detects mRNA, or rather the more stable cDNA MB206

  3. The cDNA Microarray Technique • High-throughput measuring- 5000-20000 gene expressions at the same time • Identify genes that behaves different in different cell populations- tumor cells vs healthy cells- brain cells vs liver cells- same tissue different organisms • Time series experiments- gene expressions over time after treatment

  4. cDNA clones (probes) excitation red laser green laser PCR product amplification purification emission Reference sample Tumor sample printing RNA RNA cDNA cDNA overlay images and normalise Hybridize 0.1nl / spot Overview scanning microarray analysis

  5. Creating the slides

  6. Reference sample Tumor sample RNA RNA cDNA cDNA Hybridize RNA Extraction & Hybridization

  7. Example of a cDNA Microarray

  8. Scanning & Image Analysis

  9. Data Output

  10. Biological question Differentially expressed genes Sample class prediction etc. Experimental design Microarray experiment 16-bit TIFF files Image analysis (Rfg, Rbg), (Gfg, Gbg) Normalization R, G Estimation Testing Clustering Discrimination Biological verification and interpretation

  11. Reading an array • Laser scans array and produces images • One laser for each color, e.g. one for green, one for red • Image analysis, main tasks: • Noise suppression • Spot localization and detection, including the extraction of the background intensity, the spot position, and the spot boundary and size • Data quantification and quality assessment • Image Analysis is a book on its own: • Kamberova, G. & Shah, S. “DNA Array Image Analysis Nuts & Bolts“. DNA Press LLC, 2002 MB206

  12. Data Transformation Transformed data {(M,A)}n=1..5184: M = log2(R/G) (ratio), A = log2(R·G)1/2 = 1/2·log2(R·G) (intensity signal)  R=(22A+M)1/2, G=(22A-M)1/2 “Observed” data {(R,G)}n=1..5184: R= red channel signalG = green channel signal (background corrected or not)

  13. Normalization Biased towards the green channel & Intensity dependent artifacts

  14. Replicated measurements Scaled print-tip normalization Median Absolute Deviation (MAD) Scaling Averaging

  15. Identification of differentially expressed genes Extreme in M values? ...or extreme in some other statistics? Extreme in T values?

  16. List of genes that the biologist can understand and verify with other experiments Gene: MavgAavgT SE 2341-0.8610.9 -18.0 0.125 6412-0.7511.1 -14.7 0.102 6123-0.70 9.8 -12.2 0.121 1020.65 10.3 -14.5 0.136 20200.64 9.3 -11.9 0.118 31320.62 9.9 -14.4 0.090 4439-0.62 9.7 -14.6 0.088 2031-0.61 10.7 -13.7 0.087 657-0.60 9.2 -13.6 0.094 5020.58 10.0 -12.7 0.101 1239-0.58 9.8 -11.4 0.103 5392-0.57 9.9 -20.7 0.057 39210.52 11.3 13.5 0.083 ...

  17. Time Course Gene Expression Profiles

  18. Statistical Problems • Image analysis- what is foreground?- what is background? • Quality- which spots can we trust?- which slides can we trust? • Artifacts from preparing the RNA, the printing, the scanning etc. • Data cleanup • Normalization within an experiment:- when few genes change.- when many genes change.- dye-swap to minimize dye effects. • Normalization between experiments:- location and scaleeffects. • What is noise and what is variability? • Which genes are actuallyup- and down regulated? • P-values. • Planning of experiments:- what is best design?- what is an optimal sample sizes? • Classification:- of samples.- of genes. • Clustering:- of samples.- of genes. • Time course experiments. • Gene networks.- identification of pathways • ...

  19. Overview of Example Brown & Botstein, 1999 MB206