Vorlesung Microarray Datenanalyse Kapitel 1: Einführung ... - Lectures

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TTest PROBLEMS • There are many genes (> tests) but only few repetitions • is using „s“ as estimate good? • if measured variance is small T becomes easily very large Therefore: for microarray it is reasonable to use a modfied version of the T test
Fudge Factors: You need to estimate the variance from data You might underestimate a already small variance (constantly expressed genes) The denominator in T becomes really small Constantly expressed genes show up on top of the list Correction: Add a constant fudge factor s 0 Regularized Tscore >Limma >SAM >Twilight
Page 1 and 2: Vorlesung Microarray Datenanalyse K
Page 3 and 4: Was sind DNAMicroarrays? •Micro
Page 5 and 6: Northern Blot Vorlesung: Microarray
Page 7 and 8: SAGE = Serial Analysis of Gene Expr
Page 9 and 10: WOMIT? - Plattformen Filter Glasc
Page 11 and 12: Nylon Filter eine Probe ra
Page 13 and 14: Grundprinzip Filter Glaschips Aff
Page 15 and 16: ! Biologie Diagnostik Therapie ...
Page 17 and 18: Beobachtung Varianz der gemessenen
Page 19: Fehler Modell Vorlesung: Microarray
Page 22 and 23: Beispiel: FehlerModell Rocke and
Page 24 and 25: Daraus ergibt sich = 2 − 2 + 2 ik
Page 26 and 27: VarianzStabilisierende Transforma
Page 28 and 29: Variance stabilizing transform atio
Page 30 and 31: ! Biologie Diagnostik Therapie ...
Page 32 and 33: Is a threefold induced gene more
Page 34 and 35: Standard Deviation and Standard Err
Page 36 and 37: Ranking: Problem: Produce an ordere
Page 38 and 39: Ranking: Order Genes due to amount
Page 40 and 41: Which gene is more differentially e
Page 42 and 43: TScore Idea: Take variances into
Page 44 and 45: Change: HIGH Variance: HIGH T ? Cha
Page 48 and 49: SAM: Significance Analysis for Micr
Page 50 and 51: Different scores give different ran
Page 52 and 53: Next Question: Ok, I chose a score
Page 54 and 55: Rumors If the gene is not different
Page 56 and 57: Reminder: Type I and Type II ERROR
Page 58 and 59: Permutations and empirical pvalue
Page 60 and 61: If a gene is differentially express
Page 62 and 63: Multiple testing with only nonind
Page 64 and 65: Acctepted Rejected We test m hypoth
Page 66 and 67: FDR = False Discovery Rate Expected
Page 68 and 69: How to control the FWER? Note, that
Page 70 and 71: The FDR • Score genes and rank th
Page 72 and 73: In microarray analysis it became po
Page 74 and 75: Histograms of the pvalues of all

Vorlesung Microarray Datenanalyse Kapitel 1: Einführung ... - Lectures

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