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Mendeley readers
Attention Score in Context
| Title |
IPO: a tool for automated optimization of XCMS parameters
|
|---|---|
| Published in |
BMC Bioinformatics, April 2015
|
| DOI | 10.1186/s12859-015-0562-8 |
| Pubmed ID | |
| Authors |
Gunnar Libiseller, Michaela Dvorzak, Ulrike Kleb, Edgar Gander, Tobias Eisenberg, Frank Madeo, Steffen Neumann, Gert Trausinger, Frank Sinner, Thomas Pieber, Christoph Magnes |
| Abstract |
Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO . |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 366 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 1% |
| United Kingdom | 3 | <1% |
| Denmark | 3 | <1% |
| Austria | 2 | <1% |
| South Africa | 2 | <1% |
| Israel | 1 | <1% |
| Switzerland | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 349 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 92 | 25% |
| Researcher | 85 | 23% |
| Student > Master | 52 | 14% |
| Student > Bachelor | 24 | 7% |
| Student > Postgraduate | 16 | 4% |
| Other | 46 | 13% |
| Unknown | 51 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 81 | 22% |
| Chemistry | 71 | 19% |
| Biochemistry, Genetics and Molecular Biology | 49 | 13% |
| Medicine and Dentistry | 20 | 5% |
| Computer Science | 16 | 4% |
| Other | 55 | 15% |
| Unknown | 74 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 22 September 2015.
All research outputs
#15,329,366
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#5,159
of 7,418 outputs
Outputs of similar age
#135,410
of 238,983 outputs
Outputs of similar age from BMC Bioinformatics
#104
of 137 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 238,983 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.