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Unboxing cluster heatmaps

Overview of attention for article published in BMC Bioinformatics, February 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

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52 X users

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Title
Unboxing cluster heatmaps
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-016-1442-6
Pubmed ID
Authors

Sophie Engle, Sean Whalen, Alark Joshi, Katherine S. Pollard

Abstract

Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. We developed an approach to "unbox" the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results. The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.

X Demographics

X Demographics

The data shown below were collected from the profiles of 52 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Germany 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Canada 1 <1%
France 1 <1%
Unknown 100 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 21%
Researcher 23 21%
Student > Master 16 15%
Student > Doctoral Student 6 6%
Student > Bachelor 6 6%
Other 11 10%
Unknown 22 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 19%
Agricultural and Biological Sciences 20 19%
Computer Science 12 11%
Social Sciences 4 4%
Engineering 4 4%
Other 20 19%
Unknown 27 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 29 July 2022.
All research outputs
#990,434
of 24,166,768 outputs
Outputs from BMC Bioinformatics
#82
of 7,507 outputs
Outputs of similar age
#24,773
of 461,929 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 148 outputs
Altmetric has tracked 24,166,768 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,507 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 98% 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 461,929 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 94% of its contemporaries.
We're also able to compare this research output to 148 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 99% of its contemporaries.