scores2calls {cellHTS2} | R Documentation

## Sigmoidal transformation of the score values stored in a cellHTS object obtaining the call values for each probe.

### Description

Apply a sigmoidal transformation with parameters z0 and lambda to the summarized scored values stored in a `cellHTS` object. The obtained results are called calls and are stored in slot `assayData`, overridding its current content.

Currently this function is implemented only for single-color data.

### Usage

    scores2calls(x, z0, lambda)

### Arguments

`x` | an object of class `cellHTS` containing replicate data that have already been scored and summarized (see details).
`z0` | a numeric value giving the centre of the sigmoidal transformation. See details.
`lambda` | a numeric value (>0) that corresponds to the parameter `lambda` of the sigmoidal transformation. This value should be `>0`, but usually it makes more sense to use a value `>=1`. See details.

### Details

This function applies a sigmoidal transformation with parameters z0 and lambda to the single per-probe score values stored in a `cellHTS` object. The obtained results are called calls. The transformation is given by:

1 / (1 + exp(-lambda * (z- z0)))

where `z` are the score values, `z0` is the centre of the sigmoidal transformation, and the `lambda` is a parameter that controls the smoothness of the transformation. The higher is `lambda`, more steeper is the transition from lower to higher values. `lambda` should be `> 0`, but usually it makes more sense to use a value `>=1`.

This transformation maps the score values to the interval `[0,1]`, and is intended to expand the scale of scores with intermediate values and shrink the ones showing extreme values, therefore making the difference between intermediate phenotypes larger.

### Value

The `cellHTS` object with the call values stored in slot `assayData`. This is an object of class `assayData` corresponding to a single matrix of dimensions `Features x 1`.

### Author(s)

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

### References

Boutros, M., Bras, L.P. and Huber, W. (2006) Analysis of cell-based RNAi screens, Genome Biology 7, R66.

### See Also

`normalizePlates`, `summarizeChannels`, `scoreReplicates`, `summarizeReplicates`, `imageScreen`.

### Examples

        data(KcViabSmall)
        x <- normalizePlates(KcViabSmall, scale="multiplicative", method="median", varianceAdjust="none")
        x <- scoreReplicates(x, sign="-", method="zscore")
        x <- summarizeReplicates(x, summary="min")
        xc <- scores2calls(x, z0=1.5, lambda=2)
        plot(Data(x), Data(xc), col="blue", xlab="z-scores", ylab="calls", main=expression(1/(1+e^{-lambda *(z-z[0])})))
        if(require(splots)) {
         sp = split(Data(xc), plate(xc))
         grid.newpage()
         plotScreen(sp, zrange=c(0,1), fill=c("white", "red"), na.fill="yellow",
                   main="Calls", ncol=3L)
        }

[Package cellHTS2 version 2.2.5 Index]