readSdrf

(character) main input; may be full path or url to the file with meta-annotation. If a short project-name is given, 
it will be searched based at the location of <code>urlPrefix</code>

(character, length=1) optional checking of column-names

chCol

(character, length=1) prefix to add to search when no complete path or url is given on <code>fi</code>, defaults to proteomics-metadata-standard on github

urlPrefix

silent

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

callFrom

(logical) display additional messages for debugging

debug

Read proteomics meta-data as sdrf file 
This function allows reading proteomics meta-data from sdrf file, as they are provided on https://github.com/bigbio/proteomics-metadata-standard. 
Thus function requires the package utils being installed.

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.

Wolfgang Raffelsberger

wrProteo

Proteomics Data Analysis Functions

readSdrf function

Read proteomics meta-data as sdrf file 
 
This function allows reading proteomics meta-data from sdrf file, as they are provided on https://github.com/bigbio/proteomics-metadata-standard. 
Thus function requires the package utils being installed. — readSdrf

Read proteomics meta-data as sdrf file 
 
This function allows reading proteomics meta-data from sdrf file, as they are provided on https://github.com/bigbio/proteomics-metadata-standard. 
Thus function requires the package utils being installed.

readSdrf: Read proteomics meta-data as sdrf file This function allows reading proteomics meta-data from sdrf file, as they are provided on https://github.com/bigbio/proteomics-metadata-standard. Thus function requires the package utils being installed.

Description

Usage

Arguments

Value

See Also

Examples