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SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data

Overview of attention for article published in BMC Bioinformatics, October 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
24 Mendeley
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Title
SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data
Published in
BMC Bioinformatics, October 2019
DOI 10.1186/s12859-019-3067-z
Pubmed ID
Authors

Yuntong Li, Teresa W.M. Fan, Andrew N. Lane, Woo-Young Kang, Susanne M. Arnold, Arnold J. Stromberg, Chi Wang, Li Chen

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 25%
Researcher 5 21%
Student > Bachelor 3 13%
Professor 3 13%
Student > Master 2 8%
Other 3 13%
Unknown 2 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 29%
Computer Science 3 13%
Agricultural and Biological Sciences 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Mathematics 1 4%
Other 2 8%
Unknown 8 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 November 2019.
All research outputs
#6,170,123
of 19,662,780 outputs
Outputs from BMC Bioinformatics
#2,566
of 6,636 outputs
Outputs of similar age
#136,408
of 346,194 outputs
Outputs of similar age from BMC Bioinformatics
#252
of 594 outputs
Altmetric has tracked 19,662,780 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 6,636 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 59% 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 346,194 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 594 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.