getZfactor: Per-experiment Z'-factor of a cellHTS object
In cellHTS2: Analysis of cell-based screens - revised version of cellHTS

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

Calculates per-experiment Z'-factor of data stored in a cellHTS object. The Z'-factor is a measure that quantifies the separation between the distribution of positive and negative controls.

getZfactor(x, 
robust=TRUE,
verbose=interactive(), 
posControls, 
negControls)

`x`	a configured `cellHTS` object. See details.
`robust`	a logical, if `TRUE` the Z'-factor is calculated using the median and MAD instead of mean and standard deviation, respectively.
`verbose`	a logical, if `TRUE` the function reports some of its intermediate progress. The default is the state of `interactive()`.
`posControls`	(optional) a list or vector of regular expressions specifying the name of the positive controls. See details.
`negControls`	(optional) a vector of regular expressions specifying the name of the negative controls. See details.

x should be an already configured cellHTS object (state(x)["configured"]=TRUE), so that the information about the well annotation of the plates is available.

The per-experiment Z'-factor values are calculated for the data stored in slot assayData of x.

If robust=TRUE (default), the Z'-factor is calculated using robust estimates of location (median) and spread (mad).

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 accessed via wellAnno(x)). The length of these vectors should be equal to the current number of channels in x (dim(Data(x))[3]). 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 annotated the negative controls. The content of posControls and negControls will be passed to regexpr for pattern matching within the well annotation given in wellAnno(x) (see examples). If no controls are available for a given channel, use "" or NA for that channel. For example, posControls = c("", "(?i)^diap$") means that channel 1 has no positive controls, while diap is the positive control for channel 2.

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.

If there are different positive controls, the Z'-factor is calculated between each of the positive controls and the negative controls.

In the case of a two-way assay, where two types of "positive" controls are used in the screen ("activators" and "inhibitors"), posControls should be defined as a list with two components (called act and inh), each of which should be vectors of regular expressions of the same length as the current number of reporters (as explained above). The Z'-factor values are calculated between each type of positive control (activators or inhibitors) and the negative controls.

The function generates a list with the per-experiment Z'-factor values in each channel and each replicate. Each element of this list is a matrix with dimensions nrReplicates x nrChannels, and is named by the positive controls. In the case of a two-way assay, these elements are called activators and inhibitors, while for a one-way assay, the elements have the same name of the positive controls. See examples section.

Ligia P. Bras ligia@ebi.ac.uk

Zhang, J.H., Chung, T.D. and Oldenburg, K.R. (1999) A simple statistical parameter for use in evaluation and validation of high throughput screening assays, J. Biomol. Screen. 4(2), 67–73.

configure, writeReport

    data(KcViabSmall)
    ## pCtrls <- c("pos") 
    ## nCtrls <- c("neg") 
    ## or for safety reasons (not a problem for the current well annotation, however) 
    pCtrls <- c("^pos$") 
    nCtrls <- c("^neg$")
    zf <- getZfactor(KcViabSmall, robust=TRUE, posControls=pCtrls, negControls=nCtrls)
    
    x <- normalizePlates(KcViabSmall, scale="multiplicative", log=FALSE, method="median", varianceAdjust="none")
    zfn <- getZfactor(x)