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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
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

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17 Dimensions

Readers on

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66 Mendeley
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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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 3%
Cuba 1 2%
Unknown 63 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 32%
Researcher 12 18%
Student > Master 6 9%
Professor 4 6%
Student > Doctoral Student 4 6%
Other 9 14%
Unknown 10 15%
Readers by discipline Count As %
Mathematics 13 20%
Agricultural and Biological Sciences 10 15%
Computer Science 8 12%
Engineering 6 9%
Neuroscience 5 8%
Other 12 18%
Unknown 12 18%

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
#2,175,862
of 14,793,395 outputs
Outputs from BMC Systems Biology
#85
of 1,093 outputs
Outputs of similar age
#53,676
of 264,314 outputs
Outputs of similar age from BMC Systems Biology
#1
of 5 outputs
Altmetric has tracked 14,793,395 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,093 research outputs from this source. They receive a mean Attention Score of 3.4. 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 264,314 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 79% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them