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| class:vsn | Class to contain result of a vsn fit |
| class:vsnInput | Class to contain input data and parameters for vsn functions |
| dim,vsn-method | Class to contain result of a vsn fit |
| dim,vsnInput-method | Class to contain input data and parameters for vsn functions |
| exprs,vsn-method | Class to contain result of a vsn fit |
| getIntensityMatrix | Extract a matrix of microarray intensities from various kinds of objects |
| justvsn | Normalization with vsn |
| justvsn,AffyBatch-method | Normalization with vsn |
| justvsn,ExpressionSet-method | Normalization with vsn |
| justvsn,RGList-method | Normalization with vsn |
| justvsn-methods | Normalization with vsn |
| kidney | Intensity data for 1 cDNA slide with two adjacent tissue samples from a nephrectomy (kidney) |
| logLik,vsnInput-method | Calculate the log likelihood and its gradient for the vsn model |
| logLik-methods | Calculate the log likelihood and its gradient for the vsn model |
| lymphoma | Intensity data for 8 cDNA slides with CLL and DLBL samples from the Alizadeh et al. paper in Nature 2000 |
| meanSdPlot | Plot row standard deviations versus row means |
| meanSdPlot,ExpressionSet-method | Plot row standard deviations versus row means |
| meanSdPlot,exprSet-method | Plot row standard deviations versus row means |
| meanSdPlot,matrix-method | Plot row standard deviations versus row means |
| meanSdPlot,vsn-method | Plot row standard deviations versus row means |
| meanSdPlot-methods | Plot row standard deviations versus row means |
| ncol,vsn-method | Class to contain result of a vsn fit |
| ncol,vsnInput-method | Class to contain input data and parameters for vsn functions |
| normalize.AffyBatch.vsn | Wrapper for vsn to be used as a normalization method in the package affy |
| nrow,vsn-method | Class to contain result of a vsn fit |
| nrow,vsnInput-method | Class to contain input data and parameters for vsn functions |
| plotVsnLogLik | Calculate the log likelihood and its gradient for the vsn model |
| predict,vsn-method | Apply the vsn transformation to data |
| sagmbAssess | Simulate data and assess vsn's parameter estimation |
| sagmbSimulateData | Simulate data and assess vsn's parameter estimation |
| show,vsn-method | Class to contain result of a vsn fit |
| show,vsnInput-method | Class to contain input data and parameters for vsn functions |
| vsn | Variance stabilization and calibration for microarray data. |
| vsn-class | Class to contain result of a vsn fit |
| vsn2 | Fit the vsn model |
| vsn2,ExpressionSet-method | Fit the vsn model |
| vsn2,matrix-method | Fit the vsn model |
| vsn2,numeric-method | Fit the vsn model |
| vsn2-methods | Fit the vsn model |
| vsn2trsf | Apply the vsn transformation to data |
| vsnh | A function that transforms a matrix of microarray intensities |
| vsnInput | Class to contain input data and parameters for vsn functions |
| vsnInput-class | Class to contain input data and parameters for vsn functions |
| vsnMatrix | Fit the vsn model |
| vsnPlotPar | Plot trajectories of calibration and transformation parameters for a vsn fit |
| [,vsn-method | Class to contain result of a vsn fit |
| [,vsnInput-method | Class to contain input data and parameters for vsn functions |