LecturesPart14

LecturesPart14 - Computational Biology, Part 14 Expression...

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Computational Biology, Part 14 Expression array cluster analysis Robert F. Murphy Robert F. Murphy Copyright Copyright 2004-2006. 2004-2006. All rights reserved. All rights reserved.
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Microarray raw data Label mRNA from one sample with a red Label mRNA from one sample with a red fluorescing probe (Cy5) and mRNA from fluorescing probe (Cy5) and mRNA from another sample with a green fluorescene another sample with a green fluorescene probe (Cy3) probe (Cy3) Hybridize to a chip with specific DNAs Hybridize to a chip with specific DNAs fixed to each well fixed to each well Measure amounts of green and red Measure amounts of green and red fluorescence fluorescence
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Example microarray image
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Data extraction Adjust fluorescent intensities using Adjust fluorescent intensities using standards (as necessary) standards (as necessary) Calculate ratio of red to green fluorescence Calculate ratio of red to green fluorescence Convert to log Convert to log 2 and round to integer and round to integer Display saturated green=-3 to black = 0 to Display saturated green=-3 to black = 0 to saturated red = +3 saturated red = +3
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mRNA expression microarray data for 9800 genes (gene number shown vertically) for 0 to 24 h (time shown horizontally) after addition of serum to a human cell line that had been deprived of serum (from http://genome- www.stanford.edu/serum)
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Unsupervised clustering algorithms Many different types: Hierarchical clustering k – means clustering Self-organising maps Hill Climbing Simulated Annealing All have the same three basic tasks of: 1. Pattern representation – patterns or features in the data. 2.
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LecturesPart14 - Computational Biology, Part 14 Expression...

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