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The projection score - an evaluation criterion for variable subset selection in PCA visualization

Overview of attention for article published in BMC Bioinformatics, July 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

blogs
1 blog
patent
1 patent

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
76 Mendeley
citeulike
4 CiteULike
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Title
The projection score - an evaluation criterion for variable subset selection in PCA visualization
Published in
BMC Bioinformatics, July 2011
DOI 10.1186/1471-2105-12-307
Pubmed ID
Authors

Magnus Fontes, Charlotte Soneson

Abstract

In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization.

Mendeley readers

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 States 4 5%
Brazil 1 1%
Israel 1 1%
United Kingdom 1 1%
India 1 1%
Spain 1 1%
Belgium 1 1%
Unknown 66 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 34%
Researcher 20 26%
Student > Master 5 7%
Student > Postgraduate 4 5%
Student > Bachelor 4 5%
Other 8 11%
Unknown 9 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 33%
Biochemistry, Genetics and Molecular Biology 13 17%
Computer Science 5 7%
Chemistry 5 7%
Mathematics 4 5%
Other 15 20%
Unknown 9 12%
Attention Score in Context

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 27 October 2014.
All research outputs
#3,778,102
of 22,764,165 outputs
Outputs from BMC Bioinformatics
#1,455
of 7,273 outputs
Outputs of similar age
#20,178
of 119,721 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 84 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,273 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 79% 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 119,721 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 82% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.