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By Eliot H. Ohlstein, Anthony G. Johnson, John D. Elliott (auth.), Richard S. Larson (eds.)
Comprises absolutely up to date protocols at the state of the art of the fields progress
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Extra info for Bioinformatics and Drug Discovery
For example, because the reverse transcription reaction begins at the 3'-end, if it was inefficient the probe sets from the 3'-ends of the housekeeping genes would give much stronger hybridization signals than the probe sets from the 5'-ends. In general, the ratio of the signals from the 3'-end to the 5'-end probe sets should be less than three. In addition, the housekeeping gene probe sets should have robust signals, as expected for transcripts expressed at high levels. 2. Percent Present A second type of quality score provided by the Affymetrix system is the Percent Present statistic.
This gives A (t − 1) = UEVT (6) in which U is an M ↔ M orthogonal matrix of singular vectors, E is an M ↔ (N − 1) diagonal matrix of eigenvalues, and V is an (N − 1) ↔ (N − 1) orthogonal matrix of singular vectors (see Heading 4. for a discussion of SVD). The singular vector matrices create an abstract vector space in which the original data matrix is represented. There is often a temptation to ascribe some biological significance to the vectors derived from SVD of a data matrix. This is difficult because SVD is a mathematical device, not a scientific theory, and there is no a priori reason that the matrices should have significance in biological terms.
Although scaling is not absolutely necessary, it does provide an additional quality statistic, the scaling factor. Scaling works by multiplying all the gene expression values by some constant, the scaling factor, which adjusts the average expression to some preset number, usually 500 or a similar integer. If scaling is used, the scaling factor provides an excellent quality measure. Poor-quality data sets invariably have larger scaling factors, because the labeling or hybridization was affected for all the genes represented on the GeneChip.
Bioinformatics and Drug Discovery by Eliot H. Ohlstein, Anthony G. Johnson, John D. Elliott (auth.), Richard S. Larson (eds.)