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SpeCond: a method to detect condition-specific gene expression

Overview of attention for article published in Genome Biology, October 2011
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1 Google+ user

Citations

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19 Dimensions

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100 Mendeley
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14 CiteULike
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Title
SpeCond: a method to detect condition-specific gene expression
Published in
Genome Biology, October 2011
DOI 10.1186/gb-2011-12-10-r101
Pubmed ID
Authors

Florence MG Cavalli, Richard Bourgon, Wolfgang Huber, Juan M Vaquerizas, Nicholas M Luscombe

Abstract

Transcriptomic studies routinely measure expression levels across numerous conditions. These datasets allow identification of genes that are specifically expressed in a small number of conditions. However, there are currently no statistically robust methods for identifying such genes. Here we present SpeCond, a method to detect condition-specific genes that outperforms alternative approaches. We apply the method to a dataset of 32 human tissues to determine 2,673 specifically expressed genes. An implementation of SpeCond is freely available as a Bioconductor package at http://www.bioconductor.org/packages/release/bioc/html/SpeCond.html.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 8%
United Kingdom 4 4%
Italy 3 3%
Norway 1 1%
Sweden 1 1%
Germany 1 1%
France 1 1%
India 1 1%
Russia 1 1%
Other 1 1%
Unknown 78 78%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 39%
Student > Ph. D. Student 24 24%
Professor > Associate Professor 11 11%
Professor 6 6%
Student > Master 6 6%
Other 10 10%
Unknown 4 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 62%
Biochemistry, Genetics and Molecular Biology 11 11%
Computer Science 8 8%
Medicine and Dentistry 4 4%
Engineering 2 2%
Other 4 4%
Unknown 9 9%
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 20 March 2014.
All research outputs
#16,048,318
of 25,374,917 outputs
Outputs from Genome Biology
#4,001
of 4,467 outputs
Outputs of similar age
#99,252
of 150,856 outputs
Outputs of similar age from Genome Biology
#40
of 46 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 9th percentile – i.e., 9% 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 150,856 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.