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Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity

Overview of attention for article published in BMC Genomics, January 2013
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Title
Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-14
Pubmed ID
Authors

Katie S Viljoen, Jonathan M Blackburn

Abstract

RNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Netherlands 1 2%
India 1 2%
South Africa 1 2%
Unknown 46 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 27%
Student > Ph. D. Student 8 16%
Student > Postgraduate 4 8%
Professor > Associate Professor 4 8%
Student > Master 4 8%
Other 10 20%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 43%
Biochemistry, Genetics and Molecular Biology 9 18%
Medicine and Dentistry 8 16%
Computer Science 3 6%
Unspecified 1 2%
Other 0 0%
Unknown 8 16%
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 23 July 2013.
All research outputs
#16,721,717
of 25,374,647 outputs
Outputs from BMC Genomics
#6,569
of 11,244 outputs
Outputs of similar age
#189,729
of 292,951 outputs
Outputs of similar age from BMC Genomics
#95
of 179 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 36th percentile – i.e., 36% 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 292,951 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 179 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.