| Title |
massPix: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics
|
|---|---|
| Published in |
Metabolomics, September 2017
|
| DOI | 10.1007/s11306-017-1252-5 |
| Pubmed ID | |
| Authors |
Nicholas J. Bond, Albert Koulman, Julian L. Griffin, Zoe Hall |
| Abstract |
Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools. We have developed massPix-an R package for analysing and interpreting data from MSI of lipids in tissue. massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries. Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering. massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications. |
X Demographics
The data shown below were collected from the profiles of 21 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 10% |
| United States | 2 | 10% |
| Spain | 2 | 10% |
| United Kingdom | 2 | 10% |
| Mexico | 1 | 5% |
| Argentina | 1 | 5% |
| China | 1 | 5% |
| India | 1 | 5% |
| Unknown | 9 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 57% |
| Scientists | 6 | 29% |
| Science communicators (journalists, bloggers, editors) | 3 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 65 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 35% |
| Researcher | 12 | 18% |
| Student > Master | 9 | 14% |
| Student > Bachelor | 6 | 9% |
| Professor | 2 | 3% |
| Other | 3 | 5% |
| Unknown | 10 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 18 | 28% |
| Engineering | 9 | 14% |
| Agricultural and Biological Sciences | 7 | 11% |
| Computer Science | 7 | 11% |
| Chemistry | 6 | 9% |
| Other | 6 | 9% |
| Unknown | 12 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 07 February 2018.
All research outputs
#2,972,919
of 24,903,209 outputs
Outputs from Metabolomics
#137
of 1,362 outputs
Outputs of similar age
#53,041
of 323,683 outputs
Outputs of similar age from Metabolomics
#7
of 38 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,362 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 90% 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 323,683 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 83% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.