Proteomics Data Analysis Functions
Compare in-silico digested proteomes for unique and shared peptides, c...
Molecular mass for Elements
Checking presence of knitr and rmarkdown
Extract Results From Moderated t-tests
Molecular mass for amino-acids
AUC from ROC-curves
Selective batch cleaning of sample- (ie column-) names in list
Combine Multiple Filters On NA-imputed Data
Molecular mass for amino-acids
Order Columns In List Of Matrixes And Vectors
Additional/final chek and adjustments to sample-order after readSample...
Get matrix with UniProt abbreviations for selected species as well as ...
Extract additional information to construct colum SpecType
Basic NA-imputaton (main)
Generic plotting of density distribution for quantitation import-funct...
Export Sample Meta-data from Quantification-Software as Sdrf-draft
Extract species annotation
Add arrow for expected Fold-Change to VolcanoPlot or MA-plot
Combine Multiple Proteomics Data-Sets
Accession-Numbers And Names Of UPS1 Proteins
Isolate NA-neighbours
Molecular mass from chemical formula
Histogram of content of NAs in matrix
Imputation of NA-values based on non-NA replicates
Plot ROC curves
Filter based on either number of total peptides and specific peptides ...
Read (Normalized) Quantitation Data Files Produced By AlphaPept
Read Tabulated Files Exported by DIA-NN At Protein Level
Read Tabulated Files Exported by DiaNN At Peptide Level
Read File Of Protein Sequences In Fasta Format
Read Tabulated Files Exported by FragPipe At Protein Level
Read Tabulated Files Exported by Ionbot At Peptide Level
Read tabulated files imported from MassChroQ
Read Quantitation Data-Files (proteinGroups.txt) Produced From MaxQuan...
Read Peptide Identification and Quantitation Data-Files (peptides.txt)...
Read csv files exported by OpenMS
Read xlsx, csv or tsv files exported from Proline and MS-Angel
readProtDiscovererPeptides, depreciated
Read Tabulated Files Exported By ProteomeDiscoverer At Protein Level, ...
Read Tabulated Files Exported by ProteomeDiscoverer At Peptide Level, ...
Read Tabulated Files Exported By ProteomeDiscoverer At Protein Level
Read Tabulated Files Exported by ProteomeDiscoverer At Peptide Level
Read Sample Meta-data from Quantification-Software And/Or Sdrf And Ali...
Read proteomics meta-data as sdrf file
Read annotation files from UCSC
Read protein annotation as exported from UniProt batch-conversion
Read (Normalized) Quantitation Data Files Produced By Wombat At Protei...
Remove Samples/Columns From list of matrixes
Complement Missing EntryNames In Annotation
Get Short Names of Proteomics Quantitation Software
Summarize statistical test result for plotting ROC-curves
t-test each line of 2 groups of data
Pair-wise testing robust to NA-imputation
Deprecialed Volcano-plot
Write sequences in fasta format to fileThis function writes sequences ...
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. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), Dia-NN (Demichev et al 2020 <doi:10.1038/s41592-019-0638-x>), Fragpipe (da Veiga et al 2020 <doi:10.1038/s41592-020-0912-y>), ionbot (Degroeve et al 2021 <doi:10.1101/2021.07.02.450686>), MassChroq (Valot et al 2011 <doi:10.1002/pmic.201100120>), OpenMS (Strauss et al 2021 <doi:10.1038/nmeth.3959>), ProteomeDiscoverer (Orsburn 2021 <doi:10.3390/proteomes9010015>), Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>), AlphaPept (preprint Strauss et al <doi:10.1101/2021.07.23.453379>) and Wombat-P (Bouyssie et al 2023 <doi:10.1021/acs.jproteome.3c00636>. Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 <doi:10.1021/acs.jproteome.0c00376>) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. 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 considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 <doi:10.1023/A:1010920819831>) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins.