normalizeChannels: Normalization of dual-channel data and data transformation
In cellHTS: Analysis of cell-based screens

Description Usage Arguments Details Value Author(s) See Also Examples

Normalizes and/or transforms dual-channel data xraw of a given cellHTS object by applying the function defined in fun. The default is to take the ratio between the second and first channels (r2/r1). Correction of plate-to-plate variations may also be performed.

1 2	normalizeChannels(x, fun = function(r1,r2) r2/r1, log = FALSE, adjustPlates, zscore, posControls, negControls, ...)

`x`	a cellHTS object that has already been configured. See details.
`fun`	a function defined by the user to relate the signal in the two channels `r1` and `r2`. `fun` takes two numeric vectors and returns a numeric vector of the same length. The default is to take the ratio between the second and first channels.
`log`	a logical value indicating whether the result obtained after applying `fun` should be `log2` transformed. The default is `log = FALSE`, and the data is not `log2` transformed.
`adjustPlates`	character string indicating the correction method to apply to adjust for plate-to-plate variations (and eventually well-to-well variations), after applying `fun` and eventually log transforming the values. Allowed values are `"median"`, `"mean"`, `"shorth"`, `"POC"`, `"NPI"`, `"negatives"` and `Bscore`. If `adjustPlates` is missing (the default), no plate-wise correction will be performed. See details.
`zscore`	indicates if the z-scores should be determined after normalization and transformation. If missing (default), the data will not be scored. Otherwise, it should be a character string, either "+" or "-", specifying the sign to use for the calculated z-scores. See details.
`posControls`	a vector of regular expressions giving the name of the positive control(s). See details.
`negControls`	a vector of regular expressions giving the name of the negative control(s). See details.
`...`	Further arguments that get passed on to the function implementing the normalization method chosen by `adjustPlates`. Currently, this is only used for `Bscore`.

For each plate and replicate of a two-color experiment, the function defined in fun is applied to relate the intensity values in the two channels of the cellHTS object. The default is to calculate the ratio between the second and the first channels, but other options can be defined.

If log = TRUE, the data obtained after applying fun is log2 transformed. The default is log = FALSE.

If adjustPlates is not missing, the obtained values will be further corrected for plate effects by considering the chosen normalization method. The available options are:

If adjustPlates="median" (median scaling), plates effects are corrected by dividing each measurement by the median value across wells annotated as sample in x$wellAnno, for each plate and replicate. If the data values are in log2 scale (log=TRUE), the per-plate factor is subtracted from each measurement, instead.
If adjustPlates="mean" (mean scaling), the average in the sample wells is consider instead. If the data values are in log2 scale (log=TRUE), the per-plate factor is subtracted from each measurement, instead.
If adjustPlates="shorth" (scaling by the midpoint of the shorth), for each plate and replicate, the midpoint of the shorth of the distribution of values in the wells annotated as sample is calculated. Then, every measurement is divided by this value (if log=FALSE) or subtracted by it (if log=TRUE, meaning that data have been log transformed).
If adjustPlates="POC" (percent of control), for each plate and replicate, each measurement is divided by the average of the measurements on the plate positive controls, and multiplied by 100.
If adjustPlates="negatives", for each plate and replicate, each measurement is divided by the median of the measurements on the plate negative controls. If the data values are in log2 scale (log=TRUE), the per-plate factor is subtracted from each measurement, instead.
If adjustPlates="NPI" (normalized percent inhibition), each measurement is subtracted from the average of the intensities on the plate positive controls, and this result is divided by the difference between the means of the measurements on the positive and the negative controls.
If adjustPlates="Bscore" (Bscore), for each plate and replicate, the B score method is applied to remove plate effects and row and column biases.

By default, adjustPlates is missing.

If zscore is not missing, a robust z-score for each individual measurement will be determined for each plate and each well by subtracting the overall median and dividing by the overall mad. The overall median and mad are taken by considering the distribution of intensities (over all plates) in the wells whose content is annotated as sample. The allowed values for zscore ("+" or "-") are used to set the sign of the calculated z-scores. For example, with a zscore="-" a strong decrease in the signal will be represented by a positive z-score, whereas setting zscore="+", such a phenotype will be represented by a negative z-score. This option can be set to calculate the results to the commonly used convention.

The arguments posControls and/or negControls are required for applying the normalization methods based on the control measurements (that is, when adjustPlates="POC", or adjustPlates="NPI" or adjustPlates="negatives"). posControls and negControls should be given as a vector of regular expression patterns specifying the name of the positive(s) and negative(s) controls, respectivey, as provided in the plate configuration file (and stored in x$wellAnno). The length of these vectors should be equal to the final number of reporters, which in this case is always one. By default, if posControls is not given, "pos" will be taken as the name for the wells containing positive controls. Similarly, if negControls is missing, by default "neg" will be considered as the name used to annotate the negative controls. The content of posControls and negControls will be passed to regexpr for pattern matching within the well annotation given in x$wellAnno (see examples). The arguments posControls and negControls are particularly useful in multi-channel data since the controls might be reporter-specific, or after normalizing multi-channel data.

An object of class cellHTS, which is a copy of the argument x, plus an additional slot xnorm containing the normalized data. This is an array of the same dimensions as xraw, except in the dimension corresponding to the number of channels, since the two-channel intensities have been combined into one intensity value.

Moreover, the processing status of the cellHTS object is updated in the slot state to x$state["normalized"]=TRUE.

Additional outputs may be given if adjustPlates="Bscore". Please refer to the help page of the Bscore function.

Ligia Braz ligia@ebi.ac.uk, Wolfgang Huber huber@ebi.ac.uk

normalizePlates, summarizeChannels, Bscore,

 ## Not run: 
    datadir <- system.file("DualChannelScreen", package = "cellHTS")
    x <- readPlateData("Platelist.txt", "TwoColorData", path=datadir)
    x <- configure(x, "Plateconf.txt", "Screenlog.txt", "Description.txt", path=datadir)
    table(x$wellAnno)

    ## Define the controls for the different channels:
    negControls=vector("character", length=dim(x$xraw)[4])

    ## channel 1 - gene A
    ## case-insensitive and match the empty string at the beginning and end of a line (to distinguish between "geneA" and "geneAB", for example, although this is not a problem for the well annotation in this example)

    negControls[1]= "(?i)^geneA$"  
    ## channel 2 - gene A and geneB
    negControls[2]= "(?i)^geneA$|^geneB$" 
    posControls = vector("character", length=dim(x$xraw)[4])
    ## channel 1 - no controls
    ## channel 2 - geneC and geneD
    posControls[2]="(?i)^geneC$|^geneD$"

    writeReport(x, posControls=posControls, negControls=negControls)
    x = normalizeChannels(x, fun=function(x,y) y/x, log=TRUE, adjustPlates="median")
    ## Define the controls for the normalized intensities (only one channel):
    negControls = vector("character", length=dim(x$xnorm)[4])
    ## For the single channel, the negative controls are geneA and geneB 
    negControls[1]= "(?i)^geneA$|^geneB$" 
    posControls = vector("character", length=dim(x$xnorm)[4])
    ## For the single channel, the negative controls are geneC and geneD 
    posControls[1]="(?i)^geneC$|^geneD$"
    writeReport(x, force=TRUE, plotPlateArgs=list(xrange=c(-3,3)), 
         posControls=posControls, negControls=negControls)
 
## End(Not run)