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Robust sparse canonical correlation analysis

Overview of attention for article published in BMC Systems Biology, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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1 blog
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Title
Robust sparse canonical correlation analysis
Published in
BMC Systems Biology, August 2016
DOI 10.1186/s12918-016-0317-9
Pubmed ID
Authors

Ines Wilms, Christophe Croux

Abstract

Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinations of the variables in each data set having maximal correlation. In genomics, CCA has become increasingly important to estimate the associations between gene expression data and DNA copy number change data. The identification of such associations might help to increase our understanding of the development of diseases such as cancer. However, these data sets are typically high-dimensional, containing a lot of variables relative to the number of objects. Moreover, the data sets might contain atypical observations since it is likely that objects react differently to treatments. We discuss a method for Robust Sparse CCA, thereby providing a solution to both issues. Sparse estimation produces canonical vectors with some of their elements estimated as exactly zero. As such, their interpretability is improved. Robust methods can cope with atypical observations in the data. We illustrate the good performance of the Robust Sparse CCA method by several simulation studies and three biometric examples. Robust Sparse CCA considerably outperforms its main alternatives in (1) correctly detecting the main associations between the data sets, in (2) accurately estimating these associations, and in (3) detecting outliers. Robust Sparse CCA delivers interpretable canonical vectors, while at the same time coping with outlying observations. The proposed method is able to describe the associations between high-dimensional data sets, which are nowadays commonplace in genomics. Furthermore, the Robust Sparse CCA method allows to characterize outliers.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 3%
Cuba 1 1%
Unknown 65 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 32%
Researcher 13 19%
Student > Master 6 9%
Professor 5 7%
Student > Doctoral Student 4 6%
Other 9 13%
Unknown 9 13%
Readers by discipline Count As %
Mathematics 14 21%
Agricultural and Biological Sciences 10 15%
Computer Science 9 13%
Engineering 6 9%
Neuroscience 6 9%
Other 14 21%
Unknown 9 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 03 May 2019.
All research outputs
#3,636,441
of 22,968,808 outputs
Outputs from BMC Systems Biology
#97
of 1,144 outputs
Outputs of similar age
#65,424
of 356,479 outputs
Outputs of similar age from BMC Systems Biology
#4
of 33 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 91% 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 356,479 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 81% 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 well, scoring higher than 87% of its contemporaries.