replMissingProtNames

(list) output of <code>readMaxQuantFile</code>, <code>readProtDiscovFile</code> or <code>readProlineFile</code>. 
This list must be a matrix and contain $annot with the columns designated in <code>annCol</code>.

(character) the column-names form <code>x$annot</code>) which will be used : The first column designs the
column where empty fields are searched and the 2nd and (optional) 3rd will be used to fill the empty spots in the st column

annCol

silent

(character) allow easier tracking of message(s) produced

callFrom

This function helps replacing missing EntryNames (in $annot) after reading quantification results. 
To do so the comumn-names of <code>annCol</code> will be used : 
The content of 2nd element (and optional 3rd element) will be used to replace missing content in column defined by 1st element.

Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data.
Fasta-formatted proteomes (eg from UniProt Consortium <doi:10.1093/nar/gky1049>) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted.
Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons.
The functions provided here help to inspect graphically the data to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement by low random values.
Dedicated filtering and statistical testing using the framework of package 'limma' <doi:10.18129/B9.bioc.limma> can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events.
Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 <doi:10.1038/nbt.3685>, Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>), can be run with special options separating
the data into sub-groups during normalization and testing.
As example the data-set from Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>) is provided quantified by MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), ProteomeDiscoverer,
OpenMS (<doi:10.1038/nmeth.3959>) and Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>). Meta-data provided in sdrf format can be integrated to the analysis.
Subsequently, ROC curves (Hand and Till 2001 <doi:10.1023/A:1010920819831>) can be constructed to compare multiple analysis approaches.

replMissingProtNames: Complement missing EntryNames in annotation

Description

Usage

Arguments

Value

See Also

Examples