DDARawData: Example dataset from a label-free DDA, a controlled spike-in...

Description Usage Format Details Value Author(s) References Examples


This is a data set obtained from a published study (Mueller, et. al, 2007). A controlled spike-in experiment, where 6 proteins, (horse myoglobin, bovine carbonic anhydrase, horse Cytochrome C, chicken lysozyme, yeast alcohol dehydrogenase, rabbit aldolase A) were spiked into a complex background in known concentrations in a latin square design. The experiment contained 6 mixtures, and each mixture was analyzed in label-free LC-MS mode with 3 technical replicates (resulting in the total of 18 runs). Each protein was represented by 7-21 peptides, and each peptide was represented by 1-5 transition.






The raw data (input data for MSstats) is required to contain variable of ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity. The variable names should be fixed.

If the information of one or more columns is not available for the original raw data, please retain the column variables and type in fixed value. For example, the original raw data does not contain the information of PrecursorCharge and ProductCharge, we retain the column PrecursorCharge and ProductCharge and then type in NA for all transitions in RawData.

Variable Intensity is required to be original signal without any log transformation and can be specified as the peak of height or the peak of area under curve.


data.frame with the required format of MSstats.


Meena Choi, Olga Vitek.

Maintainer: Meena Choi (mnchoi67@gmail.com)


Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" Bioinformatics, 30(17):1514-1526, 2014.

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" BMC Bioinformatics, 13:S16, 2012.

Mueller, L. N., Rinner, O., Schmidt, A., Letarte, S., Bodenmiller, B., Brusniak, M., Vitek, O., Aebersold, R., and Muller, M. (2007). SuperHirn - a novel tool for high resolution LC-MS based peptide/protein profiling. Proteomics, 7, 3470-3480. 3, 34



Example output

  ProteinName PeptideSequence PrecursorCharge FragmentIon ProductCharge
1      bovine     S.PVDIDTK_5               5          NA            NA
2      bovine     S.PVDIDTK_5               5          NA            NA
3      bovine     S.PVDIDTK_5               5          NA            NA
4      bovine     S.PVDIDTK_5               5          NA            NA
5      bovine     S.PVDIDTK_5               5          NA            NA
6      bovine     S.PVDIDTK_5               5          NA            NA
  IsotopeLabelType Condition BioReplicate Run Intensity
1                L        C1            1   1   2636792
2                L        C1            1   2   1992418
3                L        C1            1   3   1982146
4                L        C2            1   4   5019594
5                L        C2            1   5   4560468
6                L        C2            1   6   3627849

MSstats documentation built on Feb. 28, 2021, 2:01 a.m.