msprepCOPD: Example of processed mass spectrometry dataset

In marr: Maximum rank reproducibility

Description

Data contains LC-MS metabolite analysis for samples from 20 subjects. and 662 metabolites. The raw data was pre-processed using MSPrep method. The raw data pre- processing include 3 steps- Filtering, Missing Value Imputation and Normalization. Filtering- the metabolites(columns) in the raw data were removed if they were missing more than 80 percent of the samples. Missing Value Imputation- The Bayesian Principal Component Analysis (BPCA) was applied to impute the missing values. Normalization- median normalization was applied to remove unwanted variation appears from various sources in metabolomics studies. The first three columns indicate "Mass" indicating the mass-to-charge ratio, "Retention.Time", and "Compound.Name" for each present metabolite. The remaining columns indicate abundance for each of the 645 mass/retention-time combination for each subject combination.

Usage

data(msprepCOPD)

Format

SummarizedExperiment assay object containing 645 metabolites (features) of 20 subjects (samples).

Mass

Mass-to-charge ratio

Retention.Time

Retention-time

Compound.Name

Compound name for each mass/retention time combination

X10062C

The columns indicate metabolite abundances found in each subject combination. Each column begins with an 'X', followed by the subject ID.

Source

https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Project&ProjectID=PR000438

The raw data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000438. The raw data can be accessed directly via it's Project DOI: 10.21228/M8FC7C This work is supported by NIH grant, U2C- DK119886.

References

Nichole Reisdorph. Untargeted LC-MS metabolomics analysis of human COPD plasma, HILIC & C18, metabolomics_workbench, V1.

Hughes, G., Cruickshank-Quinn, C., Reisdorph, R., Lutz, S., Petrache, I., Reisdorph, N., Bowler, R. and Kechris, K., 2014. MSPrep—Summarization, normalization and diagnostics for processing of mass spectrometry–based metabolomic data. Bioinformatics, 30(1), pp.133-134.

Examples

data(msprepCOPD)

marr documentation built on March 10, 2021, 2 a.m.