get.inv.set: Wrapper where the core set is created
In rxmenezes/rscreenorm: Normalization of Multiple Genetic Screens Data

Description Usage Arguments Value See Also Examples

This function yields one core set of lethality scores per replicate, for a given rscreen.object. The core sets are used as basis for quantile-normalizing data for independent screens and their replicates.

1 2	get.inv.set(data_rscreen, my_gamma = NULL, var_type = c("mad", "IQR", "sd"), prop = 0.95, set_shape = c("left", "centre"))

`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.
`my_gamma`	scalar or numeric vector, with value(s) between 0 and a finite number. Its default value is `NULL`. If given, it will be used to construct the core sets by using the relative distance between negative and positive controls' distributions. If a numeric vector is given, it must contain as many entries as columns in the data_only slot of data_rscreen. A value of 1 means that the core set includes all lethality scores up to the ratio between the MAD of negative controls, and the sum of the MADs of negative and positive controls, assuming the robust variability measure MAD is used.
`var_type`	string indicating the statistic used as variability measure. Possible values are 'mad' when the median absolute deviation (MAD) is used (the default), 'IQR' when the inter-quartile range is used, and "sd" when the standard deviation is used.
`prop`	scalar between 0 and 1, corresponding to the desired proportion of lethality scores to be included in the core set, per replicate. The default value is 0.95. If `my_gamma` is numeric, any value for this argument is ignored.
`set_shape`	string indicating the shape of the core set. It accepts "left" (the default), when all scores to the left of the set threshold are included, per replicate, and "centre", when scores included in the core set only exclude the largest and smallest ones, at equal frequencies.

A logical matrix with the same dimensions as the data_only slot of data_rscreen.

get.rscreenorm to normalize data from multiple screens using core sets and get.leth.scores to compute scores that make observations in different screens comparable.