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DECODE: an integrated differential co-expression and differential expression analysis of gene expression data

Overview of attention for article published in BMC Bioinformatics, May 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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10 tweeters
googleplus
1 Google+ user

Citations

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14 Dimensions

Readers on

mendeley
82 Mendeley
Title
DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0582-4
Pubmed ID
Authors

Thomas WH Lui, Nancy BY Tsui, Lawrence WC Chan, Cesar SC Wong, Parco MF Siu, Benjamin YM Yung

Abstract

Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored. In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism. By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone. DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html .

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Germany 1 1%
United Kingdom 1 1%
Brazil 1 1%
United States 1 1%
Cuba 1 1%
Singapore 1 1%
Unknown 75 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 30%
Student > Ph. D. Student 23 28%
Professor > Associate Professor 7 9%
Unspecified 7 9%
Student > Doctoral Student 5 6%
Other 15 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 38%
Biochemistry, Genetics and Molecular Biology 16 20%
Computer Science 13 16%
Unspecified 11 13%
Mathematics 2 2%
Other 9 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 22 September 2015.
All research outputs
#2,984,927
of 12,378,406 outputs
Outputs from BMC Bioinformatics
#1,294
of 4,543 outputs
Outputs of similar age
#58,291
of 237,756 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 127 outputs
Altmetric has tracked 12,378,406 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 4,543 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 70% 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 237,756 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 75% of its contemporaries.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.