scholarly journals subMALDI: an open framework R package for processing irregularly-spaced mass spectrometry data

2021 ◽  
Vol 6 (65) ◽  
pp. 2694
Author(s):  
Kristen Yeh ◽  
Sophie Castel ◽  
Naomi Stock ◽  
Theresa Stotesbury ◽  
Wesley Burr
Keyword(s):  
Mass Spectrometry ◽  
R Package ◽  
Mass Spectrometry Data ◽  
Open Framework

Preliminary Metabonomic Analysis of Diabetic Rats Treated with Paired Extracts of Astragalus and Chinese Yams

2014 ◽  
Vol 997 ◽  
pp. 288-291 ◽  
Author(s):  
Li Rong Wu ◽  
Zhong Feng Shi ◽  
Wei Dong Gao ◽  
Jia Yong Zhu
Keyword(s):  
Mass Spectrometry ◽  
Blood Lipid ◽  
R Package ◽  
Diabetic Rats ◽  
Mass Spectrometry Data ◽  
Liquid Chromatography Mass Spectrometry ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Treatment Of Diabetes ◽  
Antidiabetic Effects

Combined extracts of Astragalus and the Chinese yam are an effective traditional Chinese medicine for treating diabetes. However, formal studies evaluating the effects of this combination of extracts have not been conducted. To examine the antidiabetic effects of this combination of extracts, we carried out a metabonomic study in rats. The collected liquid chromatography/mass spectrometry data were processed using the mixOmics R package. Clustered image maps (CIMs), networks, pattern recognition functions were used to perform preliminary metabonomic analysis, and the efficacy of the different treatments was evaluated. Our data demonstrated that these medicinal extracts may be useful in the treatment of diabetes through modulation of blood glucose levels and blood lipid indices. Additionally, the mixOmics package is a very useful and powerful tool for metabonomics study.

scholarly journals NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data

2013 ◽  
Vol 29 (22) ◽  
pp. 2946-2947 ◽  
Author(s):  
Elizabeth A. McClellan ◽  
Perry D. Moerland ◽  
Peter J. van der Spek ◽  
Andrew P. Stubbs
Keyword(s):  
Mass Spectrometry ◽  
Network Analysis ◽  
Biological Network ◽  
R Package ◽  
Mass Spectrometry Data ◽  
Biological Network Analysis

scholarly journals OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data

2008 ◽  
Vol 24 (6) ◽  
pp. 882-884 ◽  
Author(s):  
HyungJun Cho ◽  
Yang-jin Kim ◽  
Hee Jung Jung ◽  
Sang-Won Lee ◽  
Jae Won Lee
Keyword(s):  
Mass Spectrometry ◽  
Quantile Regression ◽  
Outlier Detection ◽  
R Package ◽  
Mass Spectrometry Data

scholarly journals Normalization of Mass Spectrometry Data (NOMAD)

2017 ◽  
Author(s):  
Carl Murie ◽  
Brian Sandri ◽  
Timothy J. Griffin ◽  
Christine Wendt ◽  
Ola Larsson
Keyword(s):  
Mass Spectrometry ◽  
Reference Sample ◽  
R Package ◽  
Mass Spectrometry Data ◽  
Bioconductor Package ◽  
Computationally Efficient ◽  
Itraq Reagent ◽  
Downstream Analysis ◽  
Larger Sample ◽  
Model Approach

AbstractMotivationiTRAQ reagent-based mass spectrometry (MS) is a commonly used technology for identification and quantification of proteins in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. iTRAQ MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that such normalization does not efficiently remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality.ResultsNOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD efficiently removes bias which allows for valid across MS run comparisons.AvailabilityThe NOMAD Bioconductor package: [email protected]; [email protected]

154: Integration of TPSA and High-Throughput Mass Spectrometry Data Improves Prostate Cancer Prediction

2007 ◽  
Vol 177 (4S) ◽  
pp. 52-53
Author(s):  
Stefano Ongarello ◽  
Eberhard Steiner ◽  
Regina Achleitner ◽  
Isabel Feuerstein ◽  
Birgit Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction

Nonparametric Pre-Processing Methods and Inference Tools for Analyzing Time-of-Flight Mass Spectrometry Data.

2007 ◽  
Vol 3 (2) ◽  
pp. 127-147 ◽  
Author(s):  
Anestis Antoniadis ◽  
Jeremie Bigot ◽  
Sophie Lambert-Lacroix ◽  
Frederique Letue
Keyword(s):  
Mass Spectrometry ◽  
Time Of Flight ◽  
Mass Spectrometry Data ◽  
Processing Methods ◽  
Flight Mass Spectrometry

Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability

2021 ◽  
Author(s):  
Trevor N. Clark ◽  
Joëlle Houriet ◽  
Warren S. Vidar ◽  
Joshua J. Kellogg ◽  
Daniel A. Todd ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Interlaboratory Comparison ◽  
Mass Spectrometry Data ◽  
Underlying Causes
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