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Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays

Overview of attention for article published in BMC Research Notes, March 2010
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
1 tweeter
q&a
1 Q&A thread

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
186 Mendeley
citeulike
10 CiteULike
Title
Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays
Published in
BMC Research Notes, March 2010
DOI 10.1186/1756-0500-3-81
Pubmed ID
Authors

Colin S Gillespie, Guiyuan Lei, Richard J Boys, Amanda Greenall, Darren J Wilkinson

Abstract

Large scale microarray experiments are becoming increasingly routine, particularly those which track a number of different cell lines through time. This time-course information provides valuable insight into the dynamic mechanisms underlying the biological processes being observed. However, proper statistical analysis of time-course data requires the use of more sophisticated tools and complex statistical models.

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

Geographical breakdown

Country Count As %
United States 8 4%
Germany 5 3%
United Kingdom 4 2%
Brazil 2 1%
India 1 <1%
Italy 1 <1%
France 1 <1%
Taiwan 1 <1%
Ireland 1 <1%
Other 3 2%
Unknown 159 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 58 31%
Student > Ph. D. Student 54 29%
Student > Master 15 8%
Other 13 7%
Student > Postgraduate 12 6%
Other 34 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 108 58%
Biochemistry, Genetics and Molecular Biology 29 16%
Computer Science 11 6%
Mathematics 10 5%
Medicine and Dentistry 10 5%
Other 18 10%

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 16 July 2012.
All research outputs
#3,006,403
of 11,185,066 outputs
Outputs from BMC Research Notes
#626
of 2,455 outputs
Outputs of similar age
#26,949
of 96,225 outputs
Outputs of similar age from BMC Research Notes
#12
of 42 outputs
Altmetric has tracked 11,185,066 research outputs across all sources so far. This one has received more attention than most of these and is in the 50th percentile.
So far Altmetric has tracked 2,455 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 69% 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 96,225 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 55% of its contemporaries.
We're also able to compare this research output to 42 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 54% of its contemporaries.