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Expression profiling & functional genomics preprocessing Exercises. Database. Database. Database. Database. Database. Database. Database. Database. Database. Database. Database. Exercises. Array by array approach. Log transformation. Filtering. Normalisation. Ratio.
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Expression profiling & functional genomicspreprocessingExercises
Exercises Array by array approach Log transformation Filtering Normalisation Ratio Test statistic (T-test)
CyberT • Use the normalized data to find statistically differentially expressed genes: CyberT software • oefnbaldi.xls • http://visitor.ics.uci.edu/genex/cybert/ The file contain the 4 normalised ratios (see SNOMAD) T test on the ratios Array 1 Per gene, per condition 4 measurements available Paired samples Array 2
Exercises • Dataset: mouse dataset • cDNA experiment:SNOMADtest2.txt • Analyze array1 by SNOMAD • Array by array normalization • ONE:measurements of the red channel • TWO: measurements of the green channel http://pevsnerlab.kennedykrieger.org/snomadinput.html http://www.bio.davidson.edu/courses/genomics/chip/chip.html
SNOMAD http://pevsnerlab.kennedykrieger.org/snomadinput.html
SNOMAD Results • Untransformed data • Note the multiplicative error • Note the influence of the Dye and condition effects
SNOMAD Results • Linear normalisation of the untransformed data • Multiplicative effects still existing (not common) • Data after log transformation • Note the removal of the multiplicative error • Note the effect of the Dye and Condition • Note the non linear character of the data
SNOMAD Results M/A plot M/A prior to normalisation
SNOMAD Results Non linear lowess fit M/A after normalisation
SNOMAD Results Original data Log transformed data Untransformed log ratio (M) Mean log intensity (A) Linearized log ratio CyberT