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Using a spike-in experiment to evaluate analysis of LC-MS data

Overview of attention for article published in Proteome Science, February 2012
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2 X users
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1 Facebook page

Citations

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17 Dimensions

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52 Mendeley
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Title
Using a spike-in experiment to evaluate analysis of LC-MS data
Published in
Proteome Science, February 2012
DOI 10.1186/1477-5956-10-13
Pubmed ID
Authors

Leepika Tuli, Tsung-Heng Tsai, Rency S Varghese, Jun Feng Xiao, Amrita Cheema, Habtom W Ressom

Abstract

Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards.

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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
Denmark 1 2%
France 1 2%
Korea, Republic of 1 2%
Unknown 48 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 33%
Student > Ph. D. Student 12 23%
Student > Master 7 13%
Student > Bachelor 5 10%
Professor > Associate Professor 2 4%
Other 3 6%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 33%
Biochemistry, Genetics and Molecular Biology 10 19%
Chemistry 10 19%
Computer Science 2 4%
Medicine and Dentistry 2 4%
Other 4 8%
Unknown 7 13%
Attention Score in Context

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 09 March 2012.
All research outputs
#13,864,183
of 22,663,969 outputs
Outputs from Proteome Science
#85
of 188 outputs
Outputs of similar age
#90,808
of 155,414 outputs
Outputs of similar age from Proteome Science
#2
of 3 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 188 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 52% 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 155,414 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
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.