| Title |
Performing statistical analyses on quantitative data in Taverna workflows: An example using R and maxdBrowse to identify differentially-expressed genes from microarray data
|
|---|---|
| Published in |
BMC Bioinformatics, August 2008
|
| DOI | 10.1186/1471-2105-9-334 |
| Pubmed ID | |
| Authors |
Peter Li, Juan I Castrillo, Giles Velarde, Ingo Wassink, Stian Soiland-Reyes, Stuart Owen, David Withers, Tom Oinn, Matthew R Pocock, Carole A Goble, Stephen G Oliver, Douglas B Kell |
| Abstract |
There has been a dramatic increase in the amount of quantitative data derived from the measurement of changes at different levels of biological complexity during the post-genomic era. However, there are a number of issues associated with the use of computational tools employed for the analysis of such data. For example, computational tools such as R and MATLAB require prior knowledge of their programming languages in order to implement statistical analyses on data. Combining two or more tools in an analysis may also be problematic since data may have to be manually copied and pasted between separate user interfaces for each tool. Furthermore, this transfer of data may require a reconciliation step in order for there to be interoperability between computational tools. |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 5% |
| Netherlands | 2 | 3% |
| United States | 2 | 3% |
| Cuba | 1 | 1% |
| Hong Kong | 1 | 1% |
| Australia | 1 | 1% |
| Sweden | 1 | 1% |
| Brazil | 1 | 1% |
| Italy | 1 | 1% |
| Other | 4 | 5% |
| Unknown | 58 | 76% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 37% |
| Student > Ph. D. Student | 12 | 16% |
| Professor | 6 | 8% |
| Other | 5 | 7% |
| Professor > Associate Professor | 5 | 7% |
| Other | 17 | 22% |
| Unknown | 3 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 36% |
| Computer Science | 20 | 26% |
| Medicine and Dentistry | 4 | 5% |
| Engineering | 4 | 5% |
| Environmental Science | 3 | 4% |
| Other | 10 | 13% |
| Unknown | 8 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 18 July 2023.
All research outputs
#2,265,718
of 24,274,366 outputs
Outputs from BMC Bioinformatics
#567
of 7,510 outputs
Outputs of similar age
#6,204
of 88,519 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 33 outputs
Altmetric has tracked 24,274,366 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,510 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 particularly well, scoring higher than 92% 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 88,519 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 33 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.