↓ Skip to main content

Methods for evaluating gene expression from Affymetrix microarray datasets

Overview of attention for article published in BMC Bioinformatics, January 2008
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
131 Mendeley
citeulike
11 CiteULike
connotea
1 Connotea
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
Methods for evaluating gene expression from Affymetrix microarray datasets
Published in
BMC Bioinformatics, January 2008
DOI 10.1186/1471-2105-9-284
Pubmed ID
Authors

Ning Jiang, Lindsey J Leach, Xiaohua Hu, Elena Potokina, Tianye Jia, Arnis Druka, Robbie Waugh, Michael J Kearsey, Zewei W Luo

Abstract

Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.

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

Geographical breakdown

Country Count As %
United Kingdom 3 2%
United States 2 2%
France 2 2%
Italy 1 <1%
Malaysia 1 <1%
Chile 1 <1%
Brazil 1 <1%
Finland 1 <1%
Germany 1 <1%
Other 2 2%
Unknown 116 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 29%
Student > Ph. D. Student 24 18%
Professor > Associate Professor 14 11%
Student > Master 11 8%
Other 10 8%
Other 26 20%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 71 54%
Biochemistry, Genetics and Molecular Biology 13 10%
Computer Science 11 8%
Medicine and Dentistry 9 7%
Mathematics 8 6%
Other 10 8%
Unknown 9 7%

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 02 July 2012.
All research outputs
#10,995,588
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#4,216
of 4,576 outputs
Outputs of similar age
#103,551
of 120,462 outputs
Outputs of similar age from BMC Bioinformatics
#53
of 57 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. 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 120,462 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.