pep_techrep_edata: Peptide-level Expression Data (e_data) with technical...
In pmartR/pmartRdata: Example Data for pmartR Package
pep_techrep_edata R Documentation
Peptide-level Expression Data (e_data) with technical replicates
Description
A dataset containing the quantified mass spectra for 21,802 unique peptides
collected on a Thermo LTQ-Orbitrap Velos Mass Spectrometer (Thermo Fisher
Corporation, San Jose, CA, USA). Columns represent technical replicates
within each of 32 biological samples (2 technical replicates per sample).
Format
A data.frame with 21,802 rows (peptides) and 65 columns (peptide
identifier and LC-MS runs). Column names have the following tags:
- Mass_Tag_ID
Peptide mass tag identifier
- 1:0, 1:7
Indicators representing dilutions of mouse plasma to
Shewanella oneidensis MR-1
- Rep1, Rep2
Indicators of technical replicates within a biological
sample
References
Webb-Robertson BJ, Matzke MM, Datta S, Payne SH, Kang J, Bramer
LM, Nicora CD, Shukla AK, Metz TO, Rodland KD, Smith RD (2014),
Bayesian proteoform modeling improves protein quantification of
global proteomic measurements. Molecular & cellular proteomics 13 (12),
3639-46.
pmartR/pmartRdata documentation built on Jan. 13, 2023, 3:52 a.m.
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| pep_techrep_edata | R Documentation |
Peptide-level Expression Data (e_data) with technical replicates
Description
A dataset containing the quantified mass spectra for 21,802 unique peptides collected on a Thermo LTQ-Orbitrap Velos Mass Spectrometer (Thermo Fisher Corporation, San Jose, CA, USA). Columns represent technical replicates within each of 32 biological samples (2 technical replicates per sample).
Format
A data.frame with 21,802 rows (peptides) and 65 columns (peptide identifier and LC-MS runs). Column names have the following tags:
- Mass_Tag_ID
Peptide mass tag identifier
- 1:0, 1:7
Indicators representing dilutions of mouse plasma to Shewanella oneidensis MR-1
- Rep1, Rep2
Indicators of technical replicates within a biological sample
References
Webb-Robertson BJ, Matzke MM, Datta S, Payne SH, Kang J, Bramer LM, Nicora CD, Shukla AK, Metz TO, Rodland KD, Smith RD (2014), Bayesian proteoform modeling improves protein quantification of global proteomic measurements. Molecular & cellular proteomics 13 (12), 3639-46.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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