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Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs

Overview of attention for article published in BMC Bioinformatics, November 2012
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1 tweeter

Citations

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101 Dimensions

Readers on

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265 Mendeley
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2 CiteULike
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Title
Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs
Published in
BMC Bioinformatics, November 2012
DOI 10.1186/1471-2105-13-s16-s6
Pubmed ID
Authors

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek

Abstract

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 265 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
United Kingdom 2 <1%
France 1 <1%
South Africa 1 <1%
India 1 <1%
Hungary 1 <1%
Belgium 1 <1%
Germany 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 251 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 82 31%
Student > Ph. D. Student 69 26%
Student > Master 25 9%
Student > Bachelor 16 6%
Other 12 5%
Other 41 15%
Unknown 20 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 104 39%
Biochemistry, Genetics and Molecular Biology 58 22%
Medicine and Dentistry 20 8%
Computer Science 13 5%
Chemistry 10 4%
Other 33 12%
Unknown 27 10%

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 20 November 2012.
All research outputs
#14,496,937
of 18,156,586 outputs
Outputs from BMC Bioinformatics
#5,434
of 6,361 outputs
Outputs of similar age
#195,061
of 264,113 outputs
Outputs of similar age from BMC Bioinformatics
#324
of 380 outputs
Altmetric has tracked 18,156,586 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,361 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 6th percentile – i.e., 6% 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 264,113 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 380 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.