calibrateMultiscan: Weighted affine calibration of a multiple re-scanned channel

Description Usage Arguments Details Value Negative, non-positive, and saturated values Missing values Weighted normalization Robustness Using a known/previously estimated offset Author(s) References See Also Examples


Weighted affine calibration of a multiple re-scanned channel.


## S3 method for class 'matrix'
calibrateMultiscan(X, weights=NULL, typeOfWeights=c("datapoint"), method="L1",
  constraint="diagonal", satSignal=2^16 - 1, ..., average=median, deviance=NULL,
  project=FALSE, .fitOnly=FALSE)



An NxK matrix (K>=2) where the columns represent the multiple scans of one channel (a two-color array contains two channels) to be calibrated.


If NULL, non-weighted normalization is done. If data-point weights are used, this should be a vector of length N of data point weights used when estimating the normalization function.


A character string specifying the type of weights given in argument weights.


A character string specifying how the estimates are robustified. See iwpca() for all accepted values.


Constraint making the bias parameters identifiable. See fitIWPCA() for more details.


Signals equal to or above this threshold is considered saturated signals.


Other arguments passed to fitIWPCA() and in turn iwpca(), e.g. center (see below).


A function to calculate the average signals between calibrated scans.


A function to calculate the deviance of the signals between calibrated scans.


If TRUE, the calibrated data points projected onto the diagonal line, otherwise not. Moreover, if TRUE, argument average is ignored.


If TRUE, the data will not be back-transform.


Fitting is done by iterated re-weighted principal component analysis (IWPCA).


If average is specified or project is TRUE, an Nx1 matrix is returned, otherwise an NxK matrix is returned. If deviance is specified, a deviance Nx1 matrix is returned as attribute deviance. In addition, the fitted model is returned as attribute modelFit.

Negative, non-positive, and saturated values

Affine multiscan calibration applies also to negative values, which are therefor also calibrated, if they exist.

Saturated signals in any scan are set to NA. Thus, they will not be used to estimate the calibration function, nor will they affect an optional projection.

Missing values

Only observations (rows) in X that contain all finite values are used in the estimation of the alibration functions. Thus, observations can be excluded by setting them to NA.

Weighted normalization

Each data point/observation, that is, each row in X, which is a vector of length K, can be assigned a weight in [0,1] specifying how much it should affect the fitting of the calibration function. Weights are given by argument weights, which should be a numeric vector of length N. Regardless of weights, all data points are calibrated based on the fitted calibration function.


By default, the model fit of multiscan calibration is done in L_1 (method="L1"). This way, outliers affect the parameter estimates less than ordinary least-square methods.

When calculating the average calibrated signal from multiple scans, by default the median is used, which further robustify against outliers.

For further robustness, downweight outliers such as saturated signals, if possible.

Tukey's biweight function is supported, but not used by default because then a "bandwidth" parameter has to selected. This can indeed be done automatically by estimating the standard deviation, for instance using MAD. However, since scanner signals have heteroscedastic noise (standard deviation is approximately proportional to the non-logged signal), Tukey's bandwidth parameter has to be a function of the signal too, cf. loess. We have experimented with this too, but found that it does not significantly improve the robustness compared to L_1. Moreover, using Tukey's biweight as is, that is, assuming homoscedastic noise, seems to introduce a (scale dependent) bias in the estimates of the offset terms.

Using a known/previously estimated offset

If the scanner offsets can be assumed to be known, for instance, from prior multiscan analyses on the scanner, then it is possible to fit the scanner model with no (zero) offset by specifying argument center=FALSE. Note that you cannot specify the offset. Instead, subtract it from all signals before calibrating, e.g. Xc <- calibrateMultiscan(X-e, center=FALSE) where e is the scanner offset (a scalar). You can assert that the model is fitted without offset by stopifnot(all(attr(Xc, "modelFit")$adiag == 0)).


Henrik Bengtsson


[1] H. Bengtsson, J. Vallon-Christersson and G. Jönsson, Calibration and assessment of channel-specific biases in microarray data with extended dynamical range, BMC Bioinformatics, 5:177, 2004.

See Also

1. Calibration and Normalization. normalizeAffine().


## Not run: # For an example, see help(normalizeAffine).

aroma.light documentation built on May 31, 2017, 3:28 p.m.