data: pipeline for preparing genomics data for statistical analysis
In frhl/genoppi-package: Genoppi

Description Usage Arguments Format Value

Process IP intensities by normalizing and calculating logfold change.

data

`bait`	a vector or string containing that should be matched in the columns containing the bait.
`infile`	the file path or data.frame that contains the raw data, i.e. accession numbers, intensity values, ratios etc.
`cols`	optional manual entry. A vector of columns that are present in the dataset header. Follows the format of columns: acession, bait1, mock1, bait2, mock2, bait3, ..
`impute`	how should missing data be imputed? NULL means that missing data rows are dropped. Will take a list containing shift and stdwidth for gaussian imputation. For further details, see `?impute`.
`transform`	charcacter. an R-command for how the data should be transformed.
`normalization`	character. an R-command for how the data should be collumn-wise transformned.
`filter`	character. only accession IDs of the filter specified are included.
`raw`	will return the data.frame alongside the raw intensity values.
`firstcol`	will change the name of the first column to the string indicated
`control`	the control or references. Normally bait vs control, but it could be bait in wildtype versus bait in mutant for differential expression.
`peptide.threshold`	how many peptides must be dectected in the ms, in order to deem it a valid observation. Default is 2.
`filter.ignore`	will try to match the inputted vector or character to acession IDs. If sucessful, it will ignore further filtering of this item. This could for instance be used, if the bait only has one unique protein, and would therefore otherwise be filtered.
`verbose`	boolean. if true, returns the table and a list with updates.