get.qnorm: Quantile-normalizes screen data using given core sets
In rxmenezes/rscreenorm: Normalization of Multiple Genetic Screens Data

Description Usage Arguments Value See Also Examples

This is a wrapper for multiple functions, that will for given core sets yield quantile-normalize scores for all replicates.

1	get.qnorm(data_rscreen, inv_set, n_perc = 1000)

`data_rscreen`	an object of class 'rscreen.object', most likely corresponding to lethality scores produced by `get.leth.scores`. It is also possible to normalize re-scaled screen viability data, such as yielded by `read.screen.data`, or by `combine.screens`, although these tend to be less comparable between cell lines and replicates than the scores.
`inv_set`	a logical matrix with the same dimensions as the 'data_only' slot in `data_rscreen`, indicating the observations that belong to the core set. This is typically the output of `sel.scores.inv.set`.
`n_perc`	number of percentiles to be used to yield a representation of each core set. The default value is 1000, which is appropriate for pooled, whole genome screens. For small, hit-picking screens a smaller number may be more appropriate. The user may check the core set sizes to decide if in doubt. Note that the value of `n_perc` should not be larger than that of any core set.

An object of class rscreen.object, with its "data_only" slot containing quantile-normalized lethality scores.

get.rscreenorm which yields core sets and quantile-normalizes data at once, get.inv.set for obtaining core sets for all replicates in the dataset and get.leth.scores to compute lethality scores.