get.leth.scores: Computes lethality scores per replicate in an rscreen.data...
In rxmenezes/rscreenorm: Normalization of Multiple Genetic Screens Data

Description Usage Arguments Value See Also Examples

get.leth.scores computes lethality scores per replicate in the data_rscreen object. The data_rscreen object may either involve data either for a single screen, or for multiple screens. Note that lethality scores are computed per plate, if a plate variable is available in the 'rscreen.object' given as input. This corrects automatically for any plate effect that affects all wells in a plate to the same extent.

1 2	get.leth.scores(data_rscreen, robust = TRUE, rescale = FALSE, scale.fun = c("asinh", "log2"))

`data_rscreen`	an object of class 'rscreen.object', either yielded by `read.screen.data`, or by `combine.screens`. Typically the 'data_only' slot of this object contains lethality scores computed by `get.leth.scores`, which already reflect a value relative to negative and positive controls, per replicate.
`robust`	logical; `TRUE` by default, when the median of the controls is used to compute the scores. If `FALSE`, the mean of the controls is used instead.
`rescale`	logical, indicating whether or not the data is to be rescaled. Defaults to `FALSE`. If `TRUE`, the screen data will be rescaled per replicate using the function indicated in 'scale.fun'.
`scale.fun`	the name of the function to be used to rescale the data. Currently the only choices are 'asinh' for the hyperbolic-arc sine transformation, and 'log2' for the log2 transformation. Note that the hyperbolic-arc sine is equivalent to the logarithm for intermediate and high values, and to a linear transformation for low values. This transformation allows for zeros, which is a useful property when transforming count data. Ignored if 'rescale' is `FALSE`.

An object of class rscreen.object, with its data_only slot containing lethality scores.

read.screen.data on reading screen data, combine.screens to combine data from multiple screens into a single object, and get.rscreenorm to normalize the screen data using the invariant-set quantile normalization.