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HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline

Overview of attention for article published in Source Code for Biology and Medicine, December 2014
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  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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2 X users
video
1 YouTube creator

Citations

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54 Dimensions

Readers on

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85 Mendeley
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Title
HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline
Published in
Source Code for Biology and Medicine, December 2014
DOI 10.1186/s13029-014-0030-2
Pubmed ID
Authors

Bohdan B Khomtchouk, Derek J Van Booven, Claes Wahlestedt

Abstract

The graphical visualization of gene expression data using heatmaps has become an integral component of modern-day medical research. Heatmaps are used extensively to plot quantitative differences in gene expression levels, such as those measured with RNAseq and microarray experiments, to provide qualitative large-scale views of the transcriptonomic landscape. Creating high-quality heatmaps is a computationally intensive task, often requiring considerable programming experience, particularly for customizing features to a specific dataset at hand.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 1%
United States 1 1%
Brazil 1 1%
Unknown 82 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 27%
Researcher 19 22%
Student > Master 9 11%
Student > Bachelor 8 9%
Professor 5 6%
Other 11 13%
Unknown 10 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 34%
Biochemistry, Genetics and Molecular Biology 16 19%
Medicine and Dentistry 7 8%
Computer Science 6 7%
Neuroscience 4 5%
Other 12 14%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 06 January 2015.
All research outputs
#15,313,289
of 22,775,504 outputs
Outputs from Source Code for Biology and Medicine
#85
of 127 outputs
Outputs of similar age
#208,714
of 353,020 outputs
Outputs of similar age from Source Code for Biology and Medicine
#4
of 10 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 127 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 25th percentile – i.e., 25% 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 353,020 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.