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Vorlesung 1 Microarray Datenanalyse Technologie, Normalisierung

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Vorlesung 1 Microarray Datenanalyse Technologie, Normalisierung

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    36. Lokale Regression Lokal gewichtete Regression. Fuer alle xi in X wird eine lineare oder polynomiale Regressions-funktion fi an die Datenpunkte in der Umgebung von xi angepasst. Sie werden bezueglich ihrer Distanz von xi gewichtet. Lokales Modell: Y = fi(X) + e. Fit: Minimiere die gewichtete Summe der Quadrate S wj (xj) (yj - fi(xj))2 Berechne die Gesamt-Regression: Y = f(X) + e, where f(xi) = fi(xi).

    37. Print-tip Normalisierung Verteilung verschiedener Pins bzw. PCR Platten koennte unterschiedlich sein. Normalisiere die Daten je separat in Gruppen.

    38. Beispiel: Print-tip loess normalization

    47. Beispiel: Fehler-Modell

    50. Varianz-Stabilisierende Transformation … = terms involving products of higher derivatives of f, and higher moments of X… = terms involving products of higher derivatives of f, and higher moments of X

    52. Die “verallgemeinerte log” Transformation

    53. Variance stabilizing transformations

    54. Robuste Parameter Schätzung After calibration, data are on a common scale and have a common distribution. Hence, can apply variance stabilization -> variance independent of kAfter calibration, data are on a common scale and have a common distribution. Hence, can apply variance stabilization -> variance independent of k

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