MSPrep—Summarization, normalization and diagnostics for processing of mass spectrometry–based metabolomic data | Zendy

Grant Hughes | Zendy; Charmion CruickshankQuinn | Zendy; Richard Reisdorph | Zendy; Sharon M. Lutz | Zendy; Irina Petrache | Zendy; Nichole Reisdorph | Zendy; Russell P. Bowler | Zendy; Katerina Kechris | Zendy

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MSPrep—Summarization, normalization and diagnostics for processing of mass spectrometry–based metabolomic data

Author(s) -

Grant Hughes,

Charmion CruickshankQuinn,

Richard Reisdorph,

Sharon M. Lutz,

Irina Petrache,

Nichole Reisdorph,

Russell P. Bowler,

Katerina Kechris

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt589

Subject(s) - automatic summarization , normalization (sociology) , computer science , metabolomics , database normalization , data mining , data processing , complement (music) , information retrieval , artificial intelligence , pattern recognition (psychology) , bioinformatics , database , biology , biochemistry , complementation , sociology , anthropology , gene , phenotype

Although R packages exist for the pre-processing of metabolomic data, they currently do not incorporate additional analysis steps of summarization, filtering and normalization of aligned data. We developed the MSPrep R package to complement other packages by providing these additional steps, implementing a selection of popular normalization algorithms and generating diagnostics to help guide investigators in their analyses.

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