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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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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

q&a
1 Q&A thread

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
194 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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 194 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%
France 1 <1%
Hong Kong 1 <1%
Sweden 1 <1%
Ireland 1 <1%
Italy 1 <1%
Other 3 2%
Unknown 167 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 60 31%
Student > Ph. D. Student 53 27%
Student > Postgraduate 16 8%
Student > Master 15 8%
Other 13 7%
Other 29 15%
Unknown 8 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 105 54%
Biochemistry, Genetics and Molecular Biology 32 16%
Medicine and Dentistry 12 6%
Computer Science 11 6%
Mathematics 11 6%
Other 14 7%
Unknown 9 5%
Attention Score in Context

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 04 August 2011.
All research outputs
#7,755,290
of 23,577,654 outputs
Outputs from BMC Research Notes
#1,263
of 4,303 outputs
Outputs of similar age
#35,127
of 95,710 outputs
Outputs of similar age from BMC Research Notes
#7
of 15 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,303 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 65% 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 95,710 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.