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A tutorial in displaying mass spectrometry-based proteomic data using heat maps

Overview of attention for article published in BMC Bioinformatics, November 2012
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Title
A tutorial in displaying mass spectrometry-based proteomic data using heat maps
Published in
BMC Bioinformatics, November 2012
DOI 10.1186/1471-2105-13-s16-s10
Pubmed ID
Authors

Melissa Key

Abstract

Data visualization plays a critical role in interpreting experimental results of proteomic experiments. Heat maps are particularly useful for this task, as they allow us to find quantitative patterns across proteins and biological samples simultaneously. The quality of a heat map can be vastly improved by understanding the options available to display and organize the data in the heat map. This tutorial illustrates how to optimize heat maps for proteomics data by incorporating known characteristics of the data into the image. First, the concepts used to guide the creating of heat maps are demonstrated. Then, these concepts are applied to two types of analysis: visualizing spectral features across biological samples, and presenting the results of tests of statistical significance. For all examples we provide details of computer code in the open-source statistical programming language R, which can be used for biologists and clinicians with little statistical background. Heat maps are a useful tool for presenting quantitative proteomic data organized in a matrix format. Understanding and optimizing the parameters used to create the heat map can vastly improve both the appearance and the interoperation of heat map data.

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X Demographics

The data shown below were collected from the profiles of 4 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 272 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 2%
United States 4 1%
Mexico 2 <1%
South Africa 1 <1%
India 1 <1%
Austria 1 <1%
Portugal 1 <1%
Korea, Republic of 1 <1%
Luxembourg 1 <1%
Other 0 0%
Unknown 254 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 74 27%
Researcher 66 24%
Student > Master 33 12%
Student > Doctoral Student 16 6%
Student > Bachelor 16 6%
Other 34 13%
Unknown 33 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 95 35%
Biochemistry, Genetics and Molecular Biology 58 21%
Medicine and Dentistry 18 7%
Chemistry 16 6%
Computer Science 7 3%
Other 38 14%
Unknown 40 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 September 2017.
All research outputs
#13,374,110
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#3,845
of 7,400 outputs
Outputs of similar age
#98,019
of 185,286 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 111 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% 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 185,286 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.