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Overview of attention for article published in Genome Biology (Online Edition), January 2002
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Mentioned by

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1 tweeter

Citations

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136 Dimensions

Readers on

mendeley
129 Mendeley
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1 CiteULike
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1 Connotea
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Title
Published in
Genome Biology (Online Edition), January 2002
DOI 10.1186/gb-2002-3-5-research0022
Pubmed ID
Authors

Wei Pan, Jizhen Lin, Chap T Le

Abstract

It has been recognized that replicates of arrays (or spots) may be necessary for reliably detecting differentially expressed genes in microarray experiments. However, the often-asked question of how many replicates are required has barely been addressed in the literature. In general, the answer depends on several factors: a given magnitude of expression change, a desired statistical power (that is, probability) to detect it, a specified Type I error rate, and the statistical method being used to detect the change. Here, we discuss how to calculate the number of replicates in the context of applying a nonparametric statistical method, the normal mixture model approach, to detect changes in gene expression. The methodology is applied to a data set containing expression levels of 1,176 genes in rats with and without pneumococcal middle-ear infection. We illustrate how to calculate the power functions for 2, 4, 6 and 8 replicates. The proposed method is potentially useful in designing microarray experiments to discover differentially expressed genes. The same idea can be applied to other statistical methods.

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

Geographical breakdown

Country Count As %
United States 6 5%
United Kingdom 4 3%
Turkey 1 <1%
Malaysia 1 <1%
France 1 <1%
Portugal 1 <1%
Sweden 1 <1%
Germany 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 111 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 43 33%
Student > Ph. D. Student 30 23%
Professor 13 10%
Student > Master 11 9%
Professor > Associate Professor 9 7%
Other 18 14%
Unknown 5 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 66 51%
Mathematics 15 12%
Biochemistry, Genetics and Molecular Biology 9 7%
Computer Science 8 6%
Medicine and Dentistry 7 5%
Other 13 10%
Unknown 11 9%

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 October 2012.
All research outputs
#10,793,704
of 13,562,740 outputs
Outputs from Genome Biology (Online Edition)
#2,886
of 3,023 outputs
Outputs of similar age
#92,553
of 130,762 outputs
Outputs of similar age from Genome Biology (Online Edition)
#6
of 7 outputs
Altmetric has tracked 13,562,740 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,023 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.8. This one is in the 1st percentile – i.e., 1% 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 130,762 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% 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.