standardize: Standardize counts relative to uncleaved counts.
In dereksonderegger/PepSeq: Tools for analyzing Peptide Sequencing results

There are two different ways to handles standardization of cleaved vs uncleaved. For each of the cleaved and uncleaved counts, we first convert to the proportion of reads, and then either look at the difference in proportion (addititive) or the ratio of the proportions (multiplicative). In the addititive case, we might want to normalize the differences based on the differing amounts in the reference library.

1	standardize(cleaved, uncleaved, ref = NULL, type = "additive")

`cleaved`	The cleaved raw counts
`uncleaved`	The uncleaved raw counts
`ref`	The raw counts of the reference library. Ideally these should be identical, but the library likely isn't equally weighted
`type`	Either 'addititive', 'multiplicative', or 'complex'.
`scale`	Should we scale to have the maximum between 100 and 1000?

df <- data.frame( cleaved   = c(20, 10,5),
                  uncleaved = c(5, 5, 2),
                  ref       = c(3, 3, 2) )
with(df, standardize( cleaved, uncleaved, ref ) )
with(df, standardize( cleaved, uncleaved, type='multiplicative' ) )