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metaX: a flexible and comprehensive software for processing metabolomics data

Overview of attention for article published in BMC Bioinformatics, March 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
18 tweeters

Citations

dimensions_citation
126 Dimensions

Readers on

mendeley
118 Mendeley
citeulike
3 CiteULike
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Title
metaX: a flexible and comprehensive software for processing metabolomics data
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1579-y
Pubmed ID
Authors

Bo Wen, Zhanlong Mei, Chunwei Zeng, Siqi Liu

Abstract

Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface ( http://metax.genomics.cn ) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/ . The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.

Twitter Demographics

The data shown below were collected from the profiles of 18 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 <1%
Unknown 117 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 20%
Researcher 20 17%
Student > Master 16 14%
Student > Bachelor 10 8%
Student > Postgraduate 6 5%
Other 19 16%
Unknown 23 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 26%
Agricultural and Biological Sciences 24 20%
Chemistry 14 12%
Computer Science 8 7%
Medicine and Dentistry 6 5%
Other 9 8%
Unknown 26 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 April 2017.
All research outputs
#2,447,036
of 18,005,056 outputs
Outputs from BMC Bioinformatics
#965
of 6,335 outputs
Outputs of similar age
#53,833
of 275,080 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 23 outputs
Altmetric has tracked 18,005,056 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,335 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,080 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.