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NeatMap - non-clustering heat map alternatives in R

Overview of attention for article published in BMC Bioinformatics, January 2010
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
51 Dimensions

Readers on

mendeley
192 Mendeley
citeulike
11 CiteULike
connotea
3 Connotea
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Title
NeatMap - non-clustering heat map alternatives in R
Published in
BMC Bioinformatics, January 2010
DOI 10.1186/1471-2105-11-45
Pubmed ID
Authors

Satwik Rajaram, Yoshi Oono

Abstract

The clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the detection of hidden structures and relations in the data. However, it is hampered by its use of cluster analysis which does not always respect the intrinsic relations in the data, often requiring non-standardized reordering of rows/columns to be performed post-clustering. This sometimes leads to uninformative and/or misleading conclusions. Often it is more informative to use dimension-reduction algorithms (such as Principal Component Analysis and Multi-Dimensional Scaling) which respect the topology inherent in the data. Yet, despite their proven utility in the analysis of biological data, they are not as widely used. This is at least partially due to the lack of user-friendly visualization methods with the visceral impact of the heat map.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 192 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 6%
Germany 4 2%
Brazil 4 2%
United Kingdom 4 2%
Belgium 3 2%
Denmark 2 1%
Canada 2 1%
Norway 2 1%
Finland 1 <1%
Other 7 4%
Unknown 152 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 80 42%
Student > Ph. D. Student 39 20%
Professor > Associate Professor 14 7%
Student > Master 12 6%
Other 10 5%
Other 29 15%
Unknown 8 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 112 58%
Biochemistry, Genetics and Molecular Biology 15 8%
Computer Science 14 7%
Environmental Science 8 4%
Medicine and Dentistry 6 3%
Other 26 14%
Unknown 11 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 04 July 2016.
All research outputs
#7,605,473
of 14,122,103 outputs
Outputs from BMC Bioinformatics
#2,874
of 5,337 outputs
Outputs of similar age
#76,585
of 188,663 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 3 outputs
Altmetric has tracked 14,122,103 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,337 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 188,663 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.