↓ Skip to main content

Impact of variance components on reliability of absolute quantification using digital PCR

Overview of attention for article published in BMC Bioinformatics, August 2014
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
57 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Impact of variance components on reliability of absolute quantification using digital PCR
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-283
Pubmed ID
Authors

Bart KM Jacobs, Els Goetghebeur, Lieven Clement

Abstract

Digital polymerase chain reaction (dPCR) is an increasingly popular technology for detecting and quantifying target nucleic acids. Its advertised strength is high precision absolute quantification without needing reference curves. The standard data analytic approach follows a seemingly straightforward theoretical framework but ignores sources of variation in the data generating process. These stem from both technical and biological factors, where we distinguish features that are 1) hard-wired in the equipment, 2) user-dependent and 3) provided by manufacturers but may be adapted by the user. The impact of the corresponding variance components on the accuracy and precision of target concentration estimators presented in the literature is studied through simulation.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 4%
Sweden 1 2%
Germany 1 2%
Unknown 53 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 32%
Student > Ph. D. Student 11 19%
Student > Master 5 9%
Other 5 9%
Professor > Associate Professor 5 9%
Other 6 11%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 39%
Biochemistry, Genetics and Molecular Biology 10 18%
Engineering 4 7%
Medicine and Dentistry 3 5%
Physics and Astronomy 2 4%
Other 7 12%
Unknown 9 16%

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 03 September 2014.
All research outputs
#10,237,990
of 13,426,363 outputs
Outputs from BMC Bioinformatics
#3,842
of 4,990 outputs
Outputs of similar age
#121,940
of 198,802 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 7 outputs
Altmetric has tracked 13,426,363 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,990 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 17th percentile – i.e., 17% 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 198,802 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.