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X Demographics
Mendeley readers
Attention Score in Context
| Title |
QCScreen: a software tool for data quality control in LC-HRMS based metabolomics
|
|---|---|
| Published in |
BMC Bioinformatics, October 2015
|
| DOI | 10.1186/s12859-015-0783-x |
| Pubmed ID | |
| Authors |
Alexandra Maria Simader, Bernhard Kluger, Nora Katharina Nicole Neumann, Christoph Bueschl, Marc Lemmens, Gerald Lirk, Rudolf Krska, Rainer Schuhmacher |
| Abstract |
Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process. The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection. QCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences. |
X Demographics
The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 6% |
| Germany | 1 | 6% |
| United States | 1 | 6% |
| Italy | 1 | 6% |
| Unknown | 13 | 76% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 59% |
| Members of the public | 6 | 35% |
| Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Denmark | 1 | 2% |
| South Africa | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 56 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 22% |
| Student > Ph. D. Student | 11 | 18% |
| Student > Master | 6 | 10% |
| Student > Doctoral Student | 6 | 10% |
| Student > Bachelor | 5 | 8% |
| Other | 12 | 20% |
| Unknown | 7 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 23% |
| Biochemistry, Genetics and Molecular Biology | 9 | 15% |
| Medicine and Dentistry | 7 | 12% |
| Chemistry | 5 | 8% |
| Computer Science | 4 | 7% |
| Other | 10 | 17% |
| Unknown | 11 | 18% |
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 10 May 2021.
All research outputs
#3,052,591
of 22,830,751 outputs
Outputs from BMC Bioinformatics
#1,064
of 7,288 outputs
Outputs of similar age
#44,923
of 283,725 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 141 outputs
Altmetric has tracked 22,830,751 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 7,288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 85% 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 283,725 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 84% of its contemporaries.
We're also able to compare this research output to 141 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 90% of its contemporaries.