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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SECIMTools: a suite of metabolomics data analysis tools
|
|---|---|
| Published in |
BMC Bioinformatics, April 2018
|
| DOI | 10.1186/s12859-018-2134-1 |
| Pubmed ID | |
| Authors |
Alexander S. Kirpich, Miguel Ibarra, Oleksandr Moskalenko, Justin M. Fear, Joseph Gerken, Xinlei Mi, Ali Ashrafi, Alison M. Morse, Lauren M. McIntyre |
| Abstract |
Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists. SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net). SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data. |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 21% |
| United Kingdom | 3 | 21% |
| Netherlands | 1 | 7% |
| Australia | 1 | 7% |
| Germany | 1 | 7% |
| India | 1 | 7% |
| Unknown | 4 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 57% |
| Scientists | 6 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 102 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 23% |
| Researcher | 22 | 22% |
| Student > Master | 13 | 13% |
| Student > Bachelor | 5 | 5% |
| Student > Doctoral Student | 4 | 4% |
| Other | 13 | 13% |
| Unknown | 22 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 21% |
| Biochemistry, Genetics and Molecular Biology | 15 | 15% |
| Medicine and Dentistry | 8 | 8% |
| Chemistry | 7 | 7% |
| Computer Science | 5 | 5% |
| Other | 16 | 16% |
| Unknown | 30 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 28 April 2018.
All research outputs
#4,569,427
of 24,514,423 outputs
Outputs from BMC Bioinformatics
#1,681
of 7,548 outputs
Outputs of similar age
#83,923
of 331,477 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 100 outputs
Altmetric has tracked 24,514,423 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,548 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 77% 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 331,477 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.