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YuGene: A simple approach to scale gene expression data derived from different platforms for integrated analyses

Overview of attention for article published in Genomics, March 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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1 patent

Citations

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63 Dimensions

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99 Mendeley
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Title
YuGene: A simple approach to scale gene expression data derived from different platforms for integrated analyses
Published in
Genomics, March 2014
DOI 10.1016/j.ygeno.2014.03.001
Pubmed ID
Authors

Kim-Anh Lê Cao, Florian Rohart, Leo McHugh, Othmar Korn, Christine A. Wells

Abstract

Gene expression databases contain invaluable information about a range of cell states, but the question "Where is my gene of interest expressed?" remains one of the most difficult to systematically assess when relevant data is derived on different platforms. Barriers to integrating this data include disparities in data formats and scale, a lack of common identifiers, and the disproportionate contribution of a platform to the 'batch effect'. There are few purpose-built cross-platform normalization strategies, and most of these fit data to an idealized data structure, which in turn may compromise gene expression comparisons between different platforms. YuGene addresses this gap by providing a simple transform that assigns a modified cumulative proportion value to each measurement, without losing essential underlying information on data distributions or experimental correlates. The Yugene transform is applied to individual samples and is suitable to apply to data with different distributions. Yugene is robust to combining datasets of different sizes, does not require global renormalization as new data is added, and does not require a common identifier. YuGene was benchmarked against commonly used normalization approaches, performing favorably in comparison to quantile (RMA), Z-score or rank methods. Implementation in the www.stemformatics.org resource provides users with expression queries across stem cell related datasets. Probe performance statistics including poorly performing (never expressed) probes, and examples of probes/genes expressed in a sample-restricted manner are provided. The YuGene software is implemented as an R package available from CRAN.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Ireland 1 1%
Germany 1 1%
Brazil 1 1%
Unknown 94 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 32%
Student > Ph. D. Student 18 18%
Student > Bachelor 10 10%
Student > Master 8 8%
Student > Doctoral Student 6 6%
Other 15 15%
Unknown 10 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 36%
Biochemistry, Genetics and Molecular Biology 20 20%
Computer Science 7 7%
Medicine and Dentistry 5 5%
Mathematics 5 5%
Other 15 15%
Unknown 11 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 18 October 2018.
All research outputs
#5,446,629
of 25,373,627 outputs
Outputs from Genomics
#859
of 5,923 outputs
Outputs of similar age
#50,468
of 237,405 outputs
Outputs of similar age from Genomics
#1
of 13 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,923 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 68% 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 237,405 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.